{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7837, 3) (7837,) (7837, 10) (7837,)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "import itertools\n",
    "import xgboost as xgb\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfTransformer, TfidfVectorizer\n",
    "from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, precision_score, recall_score\n",
    "\n",
    "import matplotlib\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "x_train_text = pd.read_csv('data/t2e/text_train.csv')\n",
    "x_test_text = pd.read_csv('data/t2e/text_test.csv')\n",
    "\n",
    "y_train_text = x_train_text['label']\n",
    "y_test_text = x_test_text['label']\n",
    "\n",
    "x_train_audio = pd.read_csv('data/s2e/audio_train.csv')\n",
    "x_test_audio = pd.read_csv('data/s2e/audio_test.csv')\n",
    "\n",
    "\n",
    "y_train_audio = x_train_audio['label']\n",
    "y_test_audio = x_test_audio['label']\n",
    "\n",
    "y_train = y_train_audio  # since y_train_audio == y_train_text\n",
    "y_test = y_test_audio  # since y_train_audio == y_train_text\n",
    "\n",
    "print(x_train_text.shape, y_train_text.shape, x_train_audio.shape, y_train_audio.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "emotion_dict = {'ang': 0,\n",
    "                'hap': 1,\n",
    "                'sad': 2,\n",
    "                'fea': 3,\n",
    "                'sur': 4,\n",
    "                'neu': 5}\n",
    "\n",
    "emo_keys = list(['ang', 'hap', 'sad', 'fea', 'sur', 'neu'])\n",
    "\n",
    "id_to_emotion = {0: 'ang', 1: 'hap', 2: 'sad', 3: 'fea', 4: 'sur', 5: 'neu'}\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    # plt.figure(figsize=(8,8))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "def one_hot_encoder(true_labels, num_records, num_classes):\n",
    "    temp = np.array(true_labels[:num_records])\n",
    "    true_labels = np.zeros((num_records, num_classes))\n",
    "    true_labels[np.arange(num_records), temp] = 1\n",
    "    return true_labels\n",
    "\n",
    "def display_results(y_test, pred_probs, cm=True):\n",
    "    pred = np.argmax(pred_probs, axis=-1)\n",
    "    one_hot_true = one_hot_encoder(y_test, len(pred), len(emotion_dict))\n",
    "    print('Test Set Accuracy =  {0:.3f}'.format(accuracy_score(y_test, pred)))\n",
    "    print('Test Set F-score =  {0:.3f}'.format(f1_score(y_test, pred, average='macro')))\n",
    "    print('Test Set Precision =  {0:.3f}'.format(precision_score(y_test, pred, average='macro')))\n",
    "    print('Test Set Recall =  {0:.3f}'.format(recall_score(y_test, pred, average='macro')))\n",
    "    if cm:\n",
    "        plot_confusion_matrix(confusion_matrix(y_test, pred), classes=emo_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cl_weight = dict(pd.Series(x_train_audio['label']).value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Text Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9797, 2464) (7837, 2464) (1960, 2464)\n"
     ]
    }
   ],
   "source": [
    "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words='english')\n",
    "\n",
    "features_text = tfidf.fit_transform(x_train_text.append(x_test_text).transcription).toarray()\n",
    "\n",
    "x_train_text = features_text[:x_train_text.shape[0]]\n",
    "x_test_text = features_text[-x_test_text.shape[0]:]\n",
    "\n",
    "print(features_text.shape, x_train_text.shape, x_test_text.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine Text + Audio Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7837, 2472) (1960, 2472)\n"
     ]
    }
   ],
   "source": [
    "combined_x_train = np.concatenate((np.array(x_train_audio[x_train_audio.columns[2:]]), x_train_text), axis=1)\n",
    "combined_x_test = np.concatenate((np.array(x_test_audio[x_test_audio.columns[2:]]), x_test_text), axis=1)\n",
    "\n",
    "print(combined_x_train.shape, combined_x_test.shape)\n",
    "\n",
    "combined_features_dict = {}\n",
    "\n",
    "combined_features_dict['x_train'] = combined_x_train\n",
    "combined_features_dict['x_test'] = combined_x_test\n",
    "combined_features_dict['y_train'] = np.array(y_train)\n",
    "combined_features_dict['y_test'] = np.array(y_test)\n",
    "\n",
    "with open('data/combined/combined_features.pkl', 'wb') as f:\n",
    "    pickle.dump(combined_features_dict, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy =  0.659\n",
      "Test Set F-score =  0.658\n",
      "Test Set Precision =  0.711\n",
      "Test Set Recall =  0.654\n",
      "Confusion matrix, without normalization\n",
      "[[ 90  19 103   1   4   7]\n",
      " [ 19 125 116   3   5  32]\n",
      " [ 19  29 492   1   7  35]\n",
      " [  0   0   0 247   0   0]\n",
      " [  0   0   0   0 241   0]\n",
      " [ 11  20 228   4   6  96]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU4AAAEmCAYAAAAN9HleAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XWcVOX3wPHP2V1AujukW3LpFFBBWkFBpNNW9PuzuxNRUQTBwEJBpUMJQboxUEJQujuX5fz+uHdxwI0Z3Jm5w563r3k5c++de8/ODmefus8jqooxxhj/RYU7AGOMiTSWOI0xJkCWOI0xJkCWOI0xJkCWOI0xJkCWOI0xJkCWOM15IpJRRCaJyGER+fo/nKebiMxMzdjCRUQaicgf4Y7DeIvYOM7IIyK3AIOB8sBRYDXwvKr+9B/P2x24C6ivqmf/c6AeJyIKlFHVjeGOxUQWK3FGGBEZDLwJvADkB4oB7wLtU+H0VwLr00LS9IeIxIQ7BuNRqmqPCHkA2YFjQOdkjsmAk1h3uI83gQzuvqbANuB+YA+wE+jt7nsaOAPEudfoCzwFfOpz7uKAAjHu617Anzil3s1AN5/tP/m8rz6wDDjs/r++z765wLPAAvc8M4E8SfxsCfH/n0/8HYDrgfXAAeARn+NrA4uAQ+6x7wDp3X3z3J/luPvz3uxz/geBXcCYhG3ue0q516jhvi4E7AWahvu7YY/QPqzEGVnqAVcA3yZzzKNAXaAaUBUneTzms78ATgIujJMch4lITlV9EqcUO1ZVs6jqqOQCEZHMwFtAK1XNipMcVydyXC5gintsbuANYIqI5PY57BagN5APSA88kMylC+B8BoWBJ4CRwK1ATaAR8LiIlHCPjQfuA/LgfHbNgdsBVLWxe0xV9+cd63P+XDil7wG+F1bVTThJ9VMRyQR8CHysqnOTiddchixxRpbcwD5NvirdDXhGVfeo6l6ckmR3n/1x7v44VZ2KU9oqd4nxnAMqi0hGVd2pqr8mckxrYIOqjlHVs6r6BfA70NbnmA9Vdb2qngS+wkn6SYnDac+NA77ESYpDVfWoe/3fcP5goKorVHWxe90twPtAEz9+pidV9bQbzwVUdSSwEVgCFMT5Q2XSGEuckWU/kCeFtrdCwF8+r/9yt50/x0WJ9wSQJdBAVPU4TvV2ELBTRKaISHk/4kmIqbDP610BxLNfVePd5wmJbbfP/pMJ7xeRsiIyWUR2icgRnBJ1nmTODbBXVU+lcMxIoDLwtqqeTuFYcxmyxBlZFgGncdr1krIDp5qZoJi77VIcBzL5vC7gu1NVZ6jqNTglr99xEkpK8STEtP0SYwrEezhxlVHVbMAjgKTwnmSHmYhIFpx241HAU25ThEljLHFGEFU9jNOuN0xEOohIJhFJJyKtROQV97AvgMdEJK+I5HGP//QSL7kaaCwixUQkO/Bwwg4RyS8i7d22ztM4Vf5ziZxjKlBWRG4RkRgRuRmoCEy+xJgCkRU4AhxzS8O3XbR/N1AywHMOBZaraj+cttvh/zlKE3EscUYYVX0dZwznYzg9uluBO4Hv3EOeA5YDa4GfgZXutku51vfAWPdcK7gw2UW5cezA6Wluwr8TE6q6H2iD05O/H6dHvI2q7ruUmAL0AE7H01Gc0vDYi/Y/BXwsIodE5KaUTiYi7YGW/PNzDgZqiEi3VIvYRAQbAG+MMQGyEqcxxgTIEqcxxgTIEqcxxgTIEqcxxgQooicxyJkrjxYqUizcYVwgfYz3/hadPhuf8kEhljFddLhDSJR1lfpn1coV+1Q1b2qeMzrblapn/3WzVqL05N4ZqtoyNa8fiIhOnIWKFGPs1HnhDuMCRXJnDHcI/7J5z/Fwh/AvFQpnC3cIiTp3znup03sRQZYMURffDfaf6dmTZCiX4qgwAE6tHpbSHWBBFdGJ0xhzOREQ79XYEmOJ0xjjDQJISnfEeoMlTmOMd0R5s+37YpY4jTEeYVV1Y4wJnFXVjTEmAIKVOI0xJjBiJU5jjAmYlTiNMSYQYr3qxhgTkAgaxxkZ5eJU9umod+nYvDYdmtdizAfDADh88AD9b2lH60bV6H9LOw4fOhjSmO4c2I8yVxakXmzV89t+XruGa5s2oH6tanS5sT1HjhwJehxP/e8OmtcsRedr657fdvjQAW67tT3tm1bntlvbc+Sw89nMnTmFm1rWp0urhnRr24RVyxYFPT5fA/v1oVihfNSsVjmk1/VHfHw89WrX4MYObVM+OMjW//EH9WpVP/8omCc7w956M9xhJU6i/HuEWfgjCLENv//G+M8/4vPJcxk3YxE/zprO35s3MerdN6jToAlT5q+mToMmjHr3jZDG1bV7D8Z9N+WCbffcPpAnn32BhctW06ZdB94e8lrQ42jb6Rbe+Xj8Bds+fG8Ites3YcLcVdSu34QP3x0CQO0GTRg7bQFfTvuJJ18ZxrMP3hX0+Hx179mLCZOnh/Sa/hr29lDKla8Q7jAAKFuuHIuWrWLRslX8tHg5GTNlom37juEOKxFiidOr/tz4B1dVjyVjxkzExMQQW6chP0yfyJyZU2jfyVk6pn2nbsyZEYq1xP7RoGFjcua6cMHEjRvXU79hYwCaNm/BpAnfBj2OmnUakD17zgu2/fj9VNp0ugWANp1uYe73ToLPlDkL4latTp44EfJqVsNGjcmVy3uLTG7fto3p06bSq3ffcIfyL3Nnz6JkyVIUu/LihUc9Ikr8e4Q7zHAHEGplylVg5dKFHDq4n5MnTzB/zgx27djO/n17yZvfWf02T7787N+3N8yRQvkKFZk6aSIAE74Zx/ZtW8MSx/69e8mbz/1s8uZn/95/PpvZ0ydxQ7NY7unTmSdfGRaW+Lzm/x64j+dffJmoKO/98xr39Zd0uqlLuMNIXMI4Titxek/JMuXpc/t9DOjWgUG3dqR8xSpER1/YkyfijfFk7wz/gFEj36Np/docO3qUdOnThzskROSCj6ZZy7Z8M3s5r4/4nPfeuKTFNC8r06ZMJm/evFSvUTPcofzLmTNnmDJ5Eh1v7BzuUJKW8G8vpUeYpbnECXBDl558NXU+H4+fQbbsObiyRGly58nL3t27ANi7exe5c4d1uj8AypYrzzeTpjN34VJuvKkLJUoEugR46sidNy9797ifzZ5d5Mrz7/lra9ZpwPa/t3DwwP5Qh+cpixYtYMqUSVQoW4Ke3bvy49zZ9OnVPdxhATBz+jSqVatB/vz5wx1KEtzhSP48wiyoiVNEvhORFSLyq4gMcLcdE5HnRWSNiCwWkfzu9lLu659F5DkRORasuBKq4Tu3b+WH6RO5vkNnml5zPRPGfQbAhHGfcfW1rYN1eb/t3bMHgHPnzvHayy/Qu9/AsMTRuEUrJo/7HIDJ4z6nyTXXA/D3lk0kLC+97pfVnDlzhhw5vdfmGErPPPciG/7cyrr1m/l4zBc0adqM0R+NCXdYAHz91Zd0vtmj1fQEEVJVD/Y4zj6qekBEMgLLRGQ8kBlYrKqPisgrQH/gOWAoMFRVvxCRQUmd0E3AAwAKFi56SUENHtCNQ4cOEBOTjkefe4Ns2XPQ947BPHBbT779cgwFixTl9Xc/vqRzX6q+PbuxYN6P7N+/j0qlr+Shx57k+PFjfPD+ewC0ad+Bbj16BT2Oh+/qw4rFP3Ho4H5a1q3AoPsepvdtg3nwjp5899UYChYuysvDPgJg9rSJTP7mS2Ji0pHhiit46Z0Pz3cWhUKPW7sy/8e57Nu3j1LFi/D4E0/Tq4/3OmS84Pjx48yZ9T1vDRse7lCS5pFquD8kocQQlJOLPAUkjHsoDlwH/AhcoaoqIjcD16hqPxHZD+RX1bMikg3YoapZkjt/pSo11JbOSJktneE/WzrDP1kyRK1Q1djUPGdU9qKaod59fh17asb9qX79QAStxCkiTYEWQD1VPSEic4ErgDj9J1vHBzMGY0yEiZASZzAbC7IDB92kWR6om8Lxi4Eb3eceb4gxxqQ+GwAPMB2IEZF1wEs4iTE59wKDRWQtUBo4HMTYjDFeFCHDkYJWTVbV00CrRHZl8TlmHDDOfbkdqOu2fXYBygUrNmOMB4lAVGS03HkpyprAO+J0yx4C+oQ5HmNMqHmgNOkPzyROVZ0PVE3xQGPM5csD7Zf+8EziNMYYK3EaY0wgxJYHNsaYwFmJ0xhjAhPKW3b/C0ucxhhPcGrqljiNMSYAYiVOY4wJlCVOY4wJkCVOY4wJkCVOY4wJhLiPCBDRiTN9TJTnJg7+dduRcIfwLzEe7KkM5gTal5sTp8+GO4SQEMSTK4MmJqITpzHm8hIpVfXISO/GmDTBWX465Yef54oWkVUiMtl9XUJElojIRhEZKyLp3e0Z3Ncb3f3FUzq3JU5jjDdIAA//3AOs83n9MjBEVUsDB4GElf364qxWURoY4h6XLEucxhjPSK0Sp4gUAVoDH7ivBWjGPxOnfwx0cJ+3d1/j7m8uKVzE2jiNMZ4ggd05lEdElvu8HqGqI3xevwn8H5DVfZ0bOKSqCT1t24DC7vPCwFYAd5Xdw+7x+5K6uCVOY4xnBJA49yW1PLCItAH2qOoKd7XdVGeJ0xjjDak3yUcDoJ2IXI+zJHk2YCiQQ0Ri3FJnEZx1znD/XxTYJiIxOCv07k/uAtbGaYzxjNRo41TVh1W1iKoWx1lqfLaqdgPmAJ3cw3oCE9znE93XuPtnawoDjS1xGmM8IzWHIyXiQZwlyDfitGGOcrePAnK72wcDD6V0IquqG2M8IcDOIb+o6lxgrvv8T6B2IsecAjoHcl5LnMYY74iMG4fSXlX9zoH9KHNlQerF/rMS8c9r13Bt0wbUr1WNLje258iR4N9v/sLDd9Kmblm6t65/ftuwl5/gluvq0LNtQx6+vTtHjxwGYOe2v2l2VSF6tWtMr3aNefWJwUGL67mH7qRV7TLc0qre+W2zpn5H15b1qFcmF+t+XnXB8Rt+/4V+na6la8t6dLu+PqdPnwpabBc7deoUjerXoU7NatSsWplnn34yZNdOToWyJahVowp1a1WnYb1aYYnh1KlTXNe0Pk3r16RR7aq8/PzTAAzq24N6NSrRuE417rm9P3FxcWGJL1ES9Kp6qklzibNr9x6M+27KBdvuuX0gTz77AguXraZNuw68PeS1oMdx/Q238Pqory/YVqtBUz6ZsoCPJ/1E0RKlGPP+kPP7ChcrzkcT5/HRxHn875k3ghZX6xu6MmT0uAu2lSxbgZfe/YRqtepfsP3s2bM8df9AHnz2db6Yvoh3P5tMTEy6oMV2sQwZMjBt5iyWrFjN4uWr+H7mDJYuWRyy6ydn2szZLF62ip8WLQvL9TNkyMD4yTOZu3AFsxcsZ84PM1m+dAmdburKwhW/8OPiVZw6eZJPPx4dlviSYonToxo0bEzOXLku2LZx43rqN2wMQNPmLZg04dugx1GtVn2yZc95wbbaDZsRE+O0nlSqGsveXTuCHsfFqtduQLYcF8ZVonQ5rixZ5l/HLv1pNqXLVaJMhasAyJ4zF9HR0SGJE5x/ZFmyZAEgLi7OKT154B+VF/zrszkbh4jQ4rpW55NP9Zq12LljW5gjvZBEiV+PcEtziTMx5StUZOqkiQBM+GYc27dtDXNEMGX8Z9Rt3OL8653b/qZ3+ybc2a0Na5YtCmNk//h78yZEhHt63UiPdk0YM2JoyGOIj4+nTmx1riycn+bNW1C7dp2Qx3AxQWjX+joa1I1l9AcjUn5DkMTHx3N1g1gqlipMk6ubU7PWP/0icXFxfD32M5q1uC5s8SUmzZc4RaS4iPwSrPOnpneGf8Coke/RtH5tjh09Srr06cMaz8fvvU50dAzXtnM6+nLny8/4uWv5cMKP3Pnwczx9f3+OHwv/vJ/x8WdZs2IxT78xghFjp/HjzCksW/hjSGOIjo5myfJVbNi8leXLl/HrL+H/yv0wZz4Ll6zg24lTeX/4u/w0f15Y4oiOjmbOguWsWbeZVSuWs+63fz6bBwffRb36jahbv2FYYkuMv0nzsk6ckaRsufJ8M2k6cxcu5cabulCiRMmwxTL1m89ZOGcGT77+/vkvSPr0Gcie02leKF+5GoWKlWDr5k1hizFBvgKFqF6rPjly5eaKjJmo3/Qa/vh1TVhiyZEjB42bNOX7mdPDcn1fhQo7t0Dny5ePdu07sHzZ0rDGkz1HDho0asLsH2YC8OqLz7Jv316eefHVsMaVGEucjmgRGSkiv4rITBHJKCL9RWSZiKwRkfEikglARD4SkeEislxE1rv3m4bE3j17ADh37hyvvfwCvfsNDNWlL7B43g98PvItXhr+OVdkzHR++8ED+4iPjwdg+99b2LblTwoVLR6WGH3VadScjX/8xqmTJzh79iwrly6gROlyIbv+3r17OXToEAAnT55k9qwfKFuufMiun5jjx49z9OjR889n/fA9FStVDnkc+/bt5bDPZ/PjnFmUKVOOTz8ezZxZ3/P+6E89Odt6pCTOYI/jLAN0VdX+IvIVcCPwjaqOBBCR53DmwnvbPb44zgDVUsAcESntDk49T0QGAAMAihQtFnBAfXt2Y8G8H9m/fx+VSl/JQ489yfHjx/jg/fcAaNO+A9169Ar8Jw3Qk/f1Y/XSBRw6uJ+OjSrR9+6HGPP+m8SdOc19vW4AoFK1WP73zBusWbaQD4a+SExMOqKionjgmdf/1YGTWh6/ty8rlzhxtW1Qif73PES2HDl5/ekHOXRgH4P73UzZClcx9KPxZMueg659bqd3x+aIQL2m19Dg6tC1me3auZP+fXtxLj6ec+fOcUOnzlzfOmR/bxO1Z/duutzk/P7iz57lpi5dufa6liGPY/eundw1qC/x8fHouXO069iJa1u1pmDOjBQpeiXXt2gEQOu2HXjgocdCHl+Swp8T/SLBWvtFnFmUv1fVMu7rB4F0wHzgOSAHkAWYoaqDROQjYJ6qjnaPnwfcraqrk7pG9RqxOmfBkqDEf6lszSH/VCmWPdwhJMqLSyEd9+CaQ/mypV+R1OxElypD/jJauJt/HYybh7RO9esHItglztM+z+OBjMBHQAdVXSMivYCmPsdc/LX14NfYGBMMIhDlwT/yiQlHI0dWYKeIpAO6XbSvs4hEiUgpoCTwR8ijM8aESeT0qofjXvXHgSXAXvf/WX32/Q0sxZk/b9DF7ZvGmMubB3KiX4KWOFV1C1DZ57XvfYzvJfG2H1R1ULBiMsZ4mxdKk/6w2ZGMMd4gVuIMmKr2CncMxpjwESKnc8gzidMYYyxxGmNMIKyqbowxgRGsc8gYYwLkjTGa/rDEaYzxjAjJm5Y4jTHeYSVOY4wJhHUOGWNMYGwcpzHGXAKrqhtjTIAiJG9a4kxt5QpmTfmgECva6N5wh/AvB5e9E+4QEnXOgzMZn/NeSMEhVuI0xpiAOAPgwx2FfyxxGmM8wgbAG2NMwCIkb1riNMZ4RAStOWSJ0xjjCTbJhzHGXAJLnMYYE6AIyZuWOI0x3mElTmOMCUQETfIRFe4AjDEGQNxxnP48kj2PyBUislRE1ojIryLytLu9hIgsEZGNIjJWRNK72zO4rze6+4unFKslTmOMZ0RHiV+PFJwGmqlqVaAa0FJE6gIvA0NUtTRwEOjrHt8XOOhuH+Iel6w0lzjvHNiPMlcWpF5s1fPbfl67hmubNqB+rWp0ubE9R44cCWlM27dtpcP1LWgQW4WGtary/rtvAfDLz2to1awhjetUo1vnDhwNUVxRUcKiLx5k/NBBADSpVZaFnz/I8q8fYeQz3YmOdr42XVrFsnTswyz76hHmfDSYq8oWDkl8CQb260OxQvmoWa1ySK+bnPV//EG9WtXPPwrmyc6wt94MeRynTp2i5dX1adagJo3rVOWVF54G4O7b+lLrqrI0bxhL84ax/LJ2dchjS46If4/kqOOY+zKd+1CgGTDO3f4x0MF93t59jbu/uaRQrE1zibNr9x6M+27KBdvuuX0gTz77AguXraZNuw68PeS1kMYUHRPD0y+8woLla5k++ydGjxjOH7//xn13DuSxZ15g3pLVXN+2Pe8MfT0k8dx5y9X8sXk34DTWf/BMd3o89CGxnV/g750HuLVtHQC27NjPtf3epNZNL/DiyOkMe6xrSOJL0L1nLyZMnh7Sa6akbLlyLFq2ikXLVvHT4uVkzJSJtu07hjyODBkyMH7STGYvWMGsn5Yz54eZrFi2BIAnnn2RWT8tZ9ZPy6lcpVrIY0uKuJN8+FlVzyMiy30eAy48l0SLyGpgD/A9sAk4pKpn3UO2AQl/6QsDWwHc/YeB3MnFmuYSZ4OGjcmZK9cF2zZuXE/9ho0BaNq8BZMmfBvSmAoUKEjVajUAyJI1K2XLlWfnjh1s2riB+g0aOXE1a8HkEMRVOF8OWjasxIffLgQgd47MnIk7y8a/9wAwe/HvdGju/GNbvGYzh46eBGDp2s0Uzp8j6PH5atioMbku+l16ydzZsyhZshTFrrwy5NcWETJnyQJAXFwcZ+PiIqLHOkr8ewD7VDXW5zHC9zyqGq+q1YAiQG2gfKrGmdQOEcmW3CM1gwi38hUqMnXSRAAmfDOO7du2hi2Wv//aws9rV1Mztjbly1dk2mQnronfjmP79uDH9er/buTRod9xzp3LbN/BY8TERFOjYjEAOraoRpH8Of/1vl4d6jNjwW9Bjy+SjPv6Szrd1CVs14+Pj6d5w1gqly5M46ubUyO2NgAvPfsEV9evwRMPP8Dp06fDFl9iUqNzyJeqHgLmAPWAHCKSMJKoCLDdfb4dKOpePwbIDuxP7rzJlTh/BX5x///rRa9/8TvySyAixUUkqNfw9c7wDxg18j2a1q/NsaNHSZc+fagufYFjx47R+9abeO6l18maLRtD3x3Jhx8Mp3mj2hw7doz06YIbV6tGldlz4Cir1l2YoHs89CGv3H8D88c8wNHjp4k/d+6C/Y1jy9CzQz0eGzohqPFFkjNnzjBl8iQ63tg5bDFER0cz66flrPptM6tWLmfdb7/w6JPP8dPyX5g+ZxEHDx7gnTdfDVt8iUmNNk4RySsiOdznGYFrgHU4CbSTe1hPIOELO9F9jbt/tmryE7MmOY5TVYsmH97lo2y58nwzyWkr27hhPTOnTw15DHFxcfS+9SY63dSVNm6bWJly5fl6wjQANm1Yz/czghtXvWoladPkKlo2rESG9OnIlvkKRj/Xgz6PfUKLvk4HR/O65SlzZb7z76lcphDvPXEL7e98jwOHjwc1vkgyc/o0qlWrQf78+cMdCtlz5KBBoybM+WEmt989GHDaQLt068l7bw8Jc3T/ECA6dZoTCgIfi0g0TuHwK1WdLCK/AV+KyHPAKmCUe/woYIyIbAQOAClWE/waAC8iXYCSqvqCiBQB8qvqCj/elxn4CqdYHA08C5QD2gIZgYXAQFVVEakJjHbfOtOfuFLL3j17yJsvH+fOneO1l1+gd7+Bobw8qsq9d/SnbLny3HbXff/EtXcPefM6cb3x6gv07DMgmbP8d0+8PZEn3naaBhrVLMO9PZrT57FPyJszC3sPHiN9uhju73UNL4+aAUDRAjn58rX+9H38k/NtoMbx9Vdf0vnm8FXT9+3bS7qYdGTPkYOTJ08yb84s7rj3AXbv2kn+AgVRVaZPmUj5ChXDFuO/BFgNT4qqrgWqJ7L9T5z2zou3nwICqhqkmDhF5B2c7vzGwAvACWA4UMuP87cEdqhqa/dc2YHvVfUZ9/UYoA0wCfgQuFNV54lIkvUHt/dsAECRosX8COFCfXt2Y8G8H9m/fx+VSl/JQ489yfHjx/jg/fcAaNO+A9169Ar4vP/FkkUL+OqLz6hYqTJN69cE4NEnn+PPTRsYPWI4AK3bdeCW7qGNK8F9PVvQqlFloqKEkV/P58dl6wF4eEArcuXIzJsP3wzA2fhzNOz2Ssji6nFrV+b/OJd9+/ZRqngRHn/iaXr16ZvyG4Ps+PHjzJn1PW8NGx62GPbs2sndg/oSfy6ec+fO0a5jJ65t2Zob21zL/v17UVUqX1WVV4YMC1uMiYmA/isAJIWqPCKyUlVriMgqVa3ublvjDi5N6b1lcUqPY4HJqjpfRG4E/g/IBOQC3sZJxGtVtZj7virA56qa7AC96jVidc6CJSn+kKF0Nt57C8TYmkP+i/fgAj/HTp1N+aAQK5A9/QpVjU3Nc+YsXlGvfnyMX8d+2y821a8fCH+q6nEiEoUzgBQRyQ2cS/4tDlVdLyI1gOuB50RkFnAHEKuqW0XkKeCKS4rcGHPZiZQSpz/jOIcB44G87j2fP+HHLUkAIlIIOKGqnwKvAjXcXftEJAtuD5c7ZOCQiDR093fz/0cwxlwuUns4UrCkWOJU1U9EZAXQwt3UWVX9HSp0FfCqiJwD4oDbcG5z+gXYBSzzObY3MFpElBB3Dhljws+foUZe4e+0ctE4iU8J4G4jVZ0BzLho83LgsUSOXQH4tpv+n7/XMcZcHlJpOFLQpZgEReRR4AugEM6wos9F5OFgB2aMSXsum6o60AOorqonAETkeZzBoy8GMzBjTNoinL8P3fP8SZw7Lzouxt1mjDGpxyOlSX8kmThFZAhOm+YB4FcRmeG+vpYLO3WMMSZVREjeTLbEmdBz/ivgO4Hl4uCFY4xJyyK+xKmqo5LaZ4wxqe2yauMUkVLA80BFfO7yUdWyQYzLGJMGRUVIidOfMZkf4UzAIUArnNmOxgYxJmNMGiTiJE5/HuHmT+LM5A5kR1U3qepjOAnUGGNSVWpMZBwK/gxHOu1O8rFJRAbhTDOfNbhhGWPSoojvHPJxH5AZuBunrTM70CeYQRlj0qYIyZt+TfKRMOHlUaB7cMMxxqRVgjfaL/2R3AD4b3Hn4EyMqt4QlIgCECVwRbrocIdxoXThDuDfvDhp8P0Tvbki5uvtPLSUhCt7Jg9+qYLBI+2X/kiuxOm9f23GmMtapMyOlNwA+FmhDMQYk7YJl1fnkDHGhMRlc+eQMcaEymWXOEUkg6qeDmYwxpi0yxncHhmZ058Z4GuLyM/ABvd1VRF5O+iRGWPSnCjx7xFu/txy+RbQBtgPoKprgKuDGZQxJu0RIDpK/HqEmz9V9ShV/euiInR8kOIxxqRhfq8EGWb+JM6tIlIbUBGJBu4C1gc3LGNMWhTAbhBXAAAgAElEQVQhTZx+Jc7bcKrrxYDdwA/uNmOMSTXikSnj/OHPvep7gC4hiMUYk8ZFSN70q1d9pIiMuPgRiuBCYeaM6VSpVI5K5Uvz6isvhTscwGLylSNjDPc0upLHWpTisRYlaVoq1wX7m5fOxbAbKpI5vTNnQYsyuXm4WUkeblaSR5uX5O2OFciULrQtZ/b7u3SR0qvuT1X9B5/nVwAdga3BCSe04uPjuffuO5gy7XsKFylCw7q1aNOmHRUqhm+iB4vpQucUvvl5N1sPnSJDTBQPXl2C3/ccY9fRM+TIGEP5/Fk4cOLM+eN/2LCfHzbsB6BygSw0K52bE3Hngh5nAvv9XTpnzSEPZEU/pPinWFXH+jw+Bm4AagY/tOBbtnQppUqVpkTJkqRPn57ON3dh8qQJFpOHYjpy6ixbD50C4PTZc+w+eoYcGZ3ZgjpVKcB3v+xGk5jDK7ZodpZvOxySOBPY7+8/EIiO8u8RbpcSQgkgf2oHEg47dmynSJGi518XLlyE7du3hzEiiyk5uTKlo0iOK9hy4CRVCmbh0Mk4th9O/Ga2dNFCxfxZWL39SEhj9Mpn5cuLMSVF/Pwv3Pxp4zwoIgfcxyHge+DhQC4iIneLyDoR+exSAzVpW4ZooX+dIoxbu4t4Va4rl5fJv+1N8virCmTlz/0nQlpNN/9NwvLAEd/GKc6o96o46wwBnFNNqmKUrNuBFqq67RLeGzSFChVm27Z/mmu3b99G4cKFwxiRxZSYKIF+dYuybOth1uw4SqFsGcidKR2PNC8JQI6M6XioWUlenfMnR04792bULJqN5VtDW02H8H9WifFiTEnxQlL0R7IlTjdJTlXVePcRcNIUkeFASWCaiDwqIqNFZKmIrBKR9u4xxUVkvoisdB/1L+mnCVBsrVps3LiBLZs3c+bMGb4e+yWt27QLxaUtpgDcWqMQu46eZvbGAwDsOHKah6au54kZG3lixkYOnYzjpdn/JM0rYqIokycza3ceDVmMCcL9WUVKTEkREb8e4eZPr/pqEamuqqsu5QKqOkhEWuLc3z4YmK2qfUQkB7BURH4A9gDXqOopESkDfAHEJnY+ERkADAAoWqzYpYR0XkxMDEOGvkPb1tcRHx9Pz159qFip0n86539lMV2oVO6M1LkyB9sPn+LhZk4Jc+Kve/h197Ek31OtUFbW7T7GmfhLqRz9N/b7u3QJVfX/fB6RosAnOH0xCoxQ1aEikgsYCxQHtgA3qepBt2Y9FLgeOAH0UtWVyV4jqUKkiMSo6lkR+RUoB2wCjrs/n6pqjQB+kC04iXA6zpCms+6uXMB1wA6cpTqq4dwHX1ZVM6V03po1Y3XBkuX+hmE8xNYcimwZ08kKVU20cHOpipa/Su8b4V9v//1NSiV5fREpCBRU1ZUikhVYAXQAegEHVPUlEXkIyKmqD4rI9Ti3kl8P1AGGqmqd5K6fXIlzKVADSM0yvQA3quofF2wUeQrnds6qOM0Hp1LxmsaYCCBATCoUOVV1J7DTfX5URNYBhYH2QFP3sI+BucCD7vZP3KbIxSKSQ0QKuudJVHKJU9wLb/qPP4evGcBdInKXqqpPE0B2YJuqnhORnoDHlq40xoRCAM2XeUTEt7o5QlX/dUejiBQHqgNLgPw+yXAX/wyrLMyFN/Vsc7ddUuLMKyKDk9qpqm8k896kPAu8CawVkShgM85cn+8C40WkB051/vglnNsYE9GEKP/HaO5LqalARLIA44F7VfWIb6eSW3C75Ebw5BJnNJAF/vtoU1Ut7vNyYCL7NwBVfDY9+F+vaYyJLM4ql6l0LpF0OEnzM1X9xt28O6EK7raD7nG3bweK+ry9CP8MwUxUcolzp6o+c4lxG2NMYFJpcLvbSz4KWHdRzXgi0BN4yf3/BJ/td4rIlzidQ4eTa98EP9o4jTEmVFJpko8GQHfgZxFZ7W57BCdhfiUifYG/gJvcfVNxetQ34gxH6p3SBZJLnM0vMWhjjAlYalXVVfUnki74/Suvub3pdwRyjSQTp6oeCORExhjzX3lhITZ/+L2uujHGBJNweS3WZowxwSd44j50f1jiNMZ4RmSkTUucxhiPiKSlMyxxGmM8IzLSpiVOY4xnCFHWq26MMf6zXnVjjLkE1qtujDEBioy0aYnThIlXZ1p/aMq6cIfwLy+1rhDuEELDxnEaY0xgrI3TGGMugZU4jTEmQBEyGskSpzHGG5yqemRkTkucxhjPiJCauiVOY4xXCGIlTmOMCYyVOI0xJgDWxmmMMYESK3EaY0zAImU+zkgZqB80M2dMp0qlclQqX5pXX3kp3OEAFlMgwhXX0b07Gf9YL8bc2YYxd7Vl1aQxF+xf+d2HDO1QkZNHDgJwYNufjH2wK+90qsqK70aHLM4EXv39+XImMvbvEW5pOnHGx8dz7913MGHSNFat/Y2vv/yCdb/9ZjFFQEzhjisqOoZGvf+P7u9M5uZXvmTttM/Zv3Uj4CTVv1YvJGveguePvyJLdpr0e4QaHVJcsjvVefX3lxjx879wS9OJc9nSpZQqVZoSJUuSPn16Ot/chcmTJlhMERBTuOPKnCsv+Uo5E5Wkz5iZXEVKcmz/HgDmjX6Zhj3vx3eun0w5clOgzFVERYe+dcyrv7/EiPj3CLc0nTh37NhOkSJFz78uXLgI27dvD2NEFlMgvBLXkd3b2fPnOgqUrcKmJbPIkjsfeUuUD3kcSfHK5+QPK3EakwacOXmcKS/fQ5O+DxMVHc2ycSOo2/WucIcVkayNM5WISFDrNYUKFWbbtq3nX2/fvo3ChQsH85Ipspj8F+644s/GMeXleynXpA2l613D4Z1bObJnO5/d25HR/VtwbP9uPh98I8cP7g1ZTIkJ9+fkP3/Lm+HPnCFJnCKSWUSmiMgaEflFRG4WkS0iksfdHysic93nT4nIGBFZAIxJ7rz/VWytWmzcuIEtmzdz5swZvh77Ja3btAvmJS2myyQuVeWHdx4nV5GS1GjfC4A8xcsy4OOf6DPyB/qM/IEsufNzyxvjyZwzb0hiSopXf3//4mdp0wslzlC1VLcEdqhqawARyQ68nMzxFYGGqnoymEHFxMQwZOg7tG19HfHx8fTs1YeKlSoF85IW02US1451K/l97kRyX1mWz+7tCED9W++lRGyTRI8/fnAvXz5wE2dOHAOJYvWkMdz69iQyZMoS9Fi9+vu7WCStqy6qGvyLiJQFZgJjgcmqOl9EtgCxqrpPRGKB11S1qYg8BaiqPp3EuQYAAwCKFitWc/2mv4Iev0k7bOkM/2RMJytUNTY1z1nhqur64bdz/Dq2XpmcqX79QISkqq6q64EawM/AcyLyBHDW5/pXXPSW48mca4SqxqpqbN484a0CGWNSmfj5CLNQtXEWAk6o6qfAqzhJdAtQ0z3kxlDEYYzxtkjpHApVG+dVwKsicg6IA24DMgKjRORZYG6I4jDGeFiENHGGJnGq6gxgRiK7yiZy7FNBD8gY40mWOI0xJgBO82VkZE5LnMYYb/DIfej+8PSdQ8aYtCW1OtVFZLSI7BGRX3y25RKR70Vkg/v/nO52EZG3RGSjiKwVkRopnd8SpzHGO1JvONJHODfe+HoImKWqZYBZ7muAVkAZ9zEAeC+lk1viNMZ4ROrdq66q84ADF21uD3zsPv8Y6OCz/RN1LAZyiEhBkmFtnMYYzwigjTOPiCz3eT1CVUek8J78qrrTfb4LyO8+Lwxs9Tlum7ttJ0mwxGmM8QQhoMS577/ccqmqKiKXfL+5VdWNMZ4R5DuHdidUwd3/73G3bweK+hxXxN2WJEucxhjPCPLSGROBnu7znsAEn+093N71usBhnyp9oqyqbozxjNQaxikiXwBNcdpCtwFPAi8BX4lIX+Av4Cb38KnA9cBG4ASQ4op6ljiNMd6QijMfqWrXJHY1T+RYBe4I5PyWOI0xnmG3XBpjTAAC7FUPq4hOnAqcjT8X7jAucDY++DPqB+rVHzeFO4R/ebR5mXCHkKjnW3lnWd8E63ceDXcIIWOJ0xhjAmRVdWOMCZCVOI0xJkARkjctcRpjPCRCMqclTmOMJ9gM8MYYE6gImgHeEqcxxjMscRpjTEC8sWa6PyxxGmM8w0qcxhgTgFSc4yPoLHEaY7wjQjJnmpvI+LYBfSlRtAC1a1Q5v+3b8V9Tq/pVZMsYw8oVy5N5d3Bs27aVtq2aU7fmVdSLrcLwYW8BcPDAATq2uY6aVcrTsc11HDp4MKhxHNm7k08f6s77A69nxKDWLP3OWddq1qiXGT6gJSNvb8u4Z+/g1LEjAMSfjWPS6w8y8ra2vD+wFQvHvh/U+BITHx9Pvdo1uLFD25BfOymHDh2iW5fOVL+qAjWqVGTJ4kVhieOzUe9yQ4s6dGxem08/GHZ+++cfDqf91TXp2Lw2Q55/PCyxJSXIM8CnmjSXOLt178m3E6desK1Cpcp8NnYcDRo2DktMMdExPPfCqyxe8TMz5yzggxHv8fu63xjy+ss0btqMFWt/p3HTZgx5/eWgxhEVHU2Lfg8x8P2p9HxjLCsnf87evzdSonoDBrw3mf7vTiJX4eIs/MpJkL/Pn87ZuDP0f28SfYZ+w6ppYzm0e1tQY7zYsLeHUq58hZBeMyX/d/+9XHPtdaz6eR2Ll68OS3wb/viN8V98zGeT5vD1jIXMmzWDv7dsYunCecydOZWvpy/k21lL6THw7pDHlpwo8e8RbmkucTZs1JicOXNdsK18+QqULVsuTBFBgYIFqVq9BgBZs2albLny7NyxnWlTJtG1Ww8AunbrwdTJE4MaR5Zc+ShQuhIAGTJlIXexkhzbt5uSNRoSFe206hQuX42j+3Y5bxAh7tRJzsWfJe7MKaJj0pEhU5agxuhr+7ZtTJ82lV69+4bsmik5fPgwC+bPo6cbU/r06cmRI0fI49i84Q+uqh5LxoyZiImJoWbdBsyaNomvx4yiz+33kT5DBgBy58kb8tiS5OeyGV7oQEpzidPr/v5rC2vXrKZmrTrs2bObAgWd5Z3zFyjAnj27QxbHod3b2L1pHYXKV71g+5qZ4ykV65TMyze8jnRXZGRot4YM63k1dW7sQ8asoUsS//fAfTz/4stERXnna/zXls3kyZuXQf37UL92De4Y1I/jx4+HPI7S5SqyculCDh3cz8mTJ/hpzkx27dzGX5s3snLpQrq1u5o+nVvxy5oVIY8teeLnI7y8840zHDt2jB633MSLr7xBtmzZLtgnIkiI/tSeOXmcb56/mxYDHrmgBLngy/eIio6m0tXtANjxx1qioqK4+9P53P7hLJZ8M5qDO7cmddpUNW3KZPLmzUv1GjVDcj1/nT17ltWrVtJvwCAWLl1JpkyZef3Vl0IeR8ky5eh9230M6taR27vfQLmKVYiOiubs2bMcPnyQTyfM5r5Hn+V/t/fCWTki/BImMrYSp/FbXFwcPW/pTOebu9K2fUcA8uXLz66dzmJ7u3buJG/efEGPI/5sHOOfv5tKTdtSvsG157ev/f4bNi6dS/v/vXY+gf86dzIlazYiOiYdmXPkpkjFGuzc8HPQYwRYtGgBU6ZMokLZEvTs3pUf586mT6/uIbl2cgoXLkLhIkWoVbsOAB1u6MSaVavCEssNXXrw5dR5fDhuOtmy5+DKkqXJX7AQzVu2Q0S4qlosUSIcPLA/LPElJjLKm0FMnCJSXETWichIEflVRGaKSEYRKSUi00VkhYjMF5Hy7vEfiUgnn/cfC1ZsXqOq3HVbf8qWq8Add993fnvL69vwxWefAPDFZ5/QqnVwe45VlSlvPkqeoiWpc8M/C/1tWj6PReM+oNOT75Huioznt2fPV5C/1iwB4MypE2z/fQ15ipYMaowJnnnuRTb8uZV16zfz8ZgvaNK0GaM/GhOSaycnf4ECFC5SlPV//AHA3DmzKF8hPJ1X+/ftBWDn9q3Mmj6RVu07c/W1bVi2aB4AW/7cQFxcHDlz5Q5LfImJlBJnsMdxlgG6qmp/EfkKuBFn6c1BqrpBROoA7wLN/D2hiAwABgAULVos4IB6d7+F+fN/ZP++fZQrVYxHHnuSnLly8b/B97Bv7146dWxLlSpV+W7y9IDPfakWL1rA2C8+pWKlq2hU16l6Pv7Us9x3/4P07t6FTz/5kKJFi/HhmC+DGse231bwy+wJ5C1elg/ubA9A056D+X74c5yNO8MXjzrJtHC5qrS66xlqtunG5CEPM2JQa1SVqtfcQL4S3lt6ItReH/IWfXvdypkzZyhRoiTvjRwdljjuH3grhw8eICZdOh559nWyZc9Bx5u788T/bueGFnVIlz49z74xPGRNQP7wwlAjf0iw2jdEpDjwvaqWcV8/CKQDHgX+8Dk0g6pWEJGPgMmqOs49/piqJttFW6NmrM5buDQI0V86W3PIP15dc8h7vz3YtNt7la+qxbKtUNXYVD1n9Zo688fFfh1bIHv6VL9+IIJd4jzt8zweyA8cUtVqiRx7FrfpQESigPRBjs0Y4yFeqYb7I9SdQ0eAzSLSGUAcCeNdtgAJXaTtcEqnxpg0xO4cSlo3oK+IrAF+Bdq720cCTdzt9YDQD34zxoRXhHSrB62qrqpbgMo+r1/z2d0ykeN3A3V9Nj0YrNiMMd7kgZzoF5sdyRjjGZHSxmmJ0xjjEd5ov/SHJU5jjCck3HIZCSxxGmM8wxKnMcYEyKrqxhgTiAgaAG+J0xjjCR4ZoukXS5zGGO+IkMxpidMY4xmR0sZpExkbYzwjtRZrE5GWIvKHiGwUkYdSPc7UPqExxlyyVLhXXUSigWFAK6Ai0FVEKqZmmJY4jTGekUqzI9UGNqrqn6p6BviSfyYTSp04vbJQ06UQkb3AX6l0ujzAvlQ6V2qxmPxjMfknNWO6UlVTdW1hEZmOE6M/rgBO+bweoaoj3PN0Alqqaj/3dXegjqremVqxRnTnUGr+4kRkeThnlE6MxeQfi8k/XozJl6r+a9Y0r7KqujHmcrMdKOrzuoi7LdVY4jTGXG6WAWVEpISIpAe6ABNT8wIRXVVPZSPCHUAiLCb/WEz+8WJMqU5Vz4rIncAMIBoYraq/puY1IrpzyBhjwsGq6sYYEyBLnMYYEyBLnOaSiIh9d0yaZV9+ExARqS8iNVT1nCVP/4lEykyTxh/2xb9IwhfcvuhJigW+EpGqXkieXv89iUhdAPVwL6zXP0MvssT5b2XB+aJ74QvlhRjgn6q5qr4FfAaMEpFK4UyeIiIJCUlEsotI9nDEkRQR6QU8LSK5wh3LxUSkm4h8Ct75rkcSS5w+RKQMsExE3gFvfKHcGK4RkftE5P4wxnEOwB0flw84A3wiItXDlTx9kuZg4COcknDnUMeRGBFpCNwEPKCqB9wZe7xkItBQRN4Fb3zXI4klTpeItAOeA94FOorIcAj/F0pE6gHvAyeBm0XkbREp4u4LaVwiUhu4F+dzugX4EBgtIpXDlTxF5DagLXArcBD4QkT6hTqORFQC8gM3ikh6VY33QmISkTIiUlBVjwJXAc1FZBSE/7seSSxxAiKSGRgMfK6qDwGVgatF5C0I3xdKRK4CegMvqepwoDGQDXg6Ia4gX//i9t44YImqbgf+BkYBG4DvRKRiQqk0VNy4juPcUjfQ3dwKeFdE+oQyFp+YrheRp1T1feANnNl+bhCR6HAmJnGUBV4GrhGR/G7yjAXai8ho8HZbrJdY4nScAv4EtgGo6kHgHqC3iDzrbgvZF8rnH1clnIlY64hIYVU9BQwCKopI4WDH4PMzX+H+fwNQVUQeVdVzqnoSWAHMxSkRBzUe3xKtW4pTVf0E53vcCnhQVb/HudXuWRHJGoZEtQeoKyL/p6qfASuBekD3hOQZ4ngA5/urquuBkcC1QDOfkucw93U+K3H6J03fqy4iJYA9qnpcRH4FPhWRmqp6AjiGc2/vtSLyvarOC0E8CcmqMLBNVb8UkT1AP6CFiMwFsrqPoJbufNoPB+K0hS3DaRdrB4x3mwv+wintXa+qO4IZD5BZVY+5Md0LlBKRPMCjOHNMbgFqi0hrYDMw0E0KISEiuVT1ALAKeBh4yk2er4jIHTh/ADMDR0IVk09sdwKlgCzA4zhzqHcGiopIRpwO0bqquifUsUWqNJs4ReQ6nL++P4rIn8BTQC5goYjMxGnDawfEu4+gc6tyrYFHROQnnITwJpAeuBvoBRwFHlbVncGOR0T6Az2A+3A+q1ic9tZrgNtxSqLdg5003fbn9kBfEbkV5/fSBvgdGKCqD4nIbzhNGU2BW0KQyH3juxr4UEQ6qOpqEfkZeAF4U0Rwk2d2VQ1H0rwN6AAMAL4BHlLVe0VEcZqkauF8n3aFOrZIliYn+RCRWjhfpmnuprY4yekBnGn38wB/4DTuvw3coKp/hiCuhridU8D9QB1gPvCQG9c9wEy3/SwY1/cd3lMep8PlNaAbcDPwA84/tqGquiAYMSQSU25gLHAncAKnLfpTnM+mDdBBVU/7HJ9dVQ+HIjb3erfjlCIrA62Bbqq61t33Ic4IhO5uaTRkEn6XIvIkTlW8J9AMuAGnthKlqqdFJJ2qxoUytstBmkucIpIBp61ut6rWcrfVBDoBuYEnVHWXiFTC6fwYqKprghhPtKrGu8/bA+uB4jg910/ilBT+wkme1wHdgc+BbxLel0px+CbN23HaDSfhlCqHqmpLESkEzAK+A55PqDoHk4hkBb7G6THPAKzDKSUdA25W1Tg3OaiqPnNR22ywYxsI9Afaq+p2EXkE54/eYKAKcDVwm6ruDUU8F8VWFqfdfhRwJbALuNVnyrV4nNqDWodQ4NJU55CIlMZpH2wMFBN32VBVXYGTDA7iJE9wOopaBytpugkBd5jK1SLSG9jpPq4D+qjqZOCAG1MRVf0Gp7S1IDWTphuHb5tmb2CCqv4FZMf5rNIBNXGqx0NCkTTduI4Cs3FqBYtwmi6K4VQ784hIF5xk9bXvzxFsbttgK5w21jj3c0sHZML5I3wT8GyYkuadwBScHvTNOMOO5rpJsxdOM8sPbgefJc1LkGbaOEWkLU4p7i+canhf4CMROaeqr6jqEhFZl9AOFczqnohkAqaIM9xpDU5V6jegIfArTi/sdnFmr64A9FXVP9y4xgcxroRk8BhwQkQG4TRXFMFJXtlwqp2h7kQYi9M7/Q7OEgj9carubXAS+62qui6UAanqSRGZCrwEbMX5g/InTgfai0CcO+ogpNz24CpAS5ze82xuTA+KSGWgOtBJVTeEOrbLSZqoqotzv/DbOG1Q1+D0lr8OLAXGAU+r6oshjqkjTvX7AE6D/RoRuQWnml4IaARsAr5Q1a9DGNcA4DYuTAb5cf7xbQ9nz6vbpDIWp2f4K5waU6ZQtmleFM8VOKW5TercHXQLTlJv7Y7MCHU8hXFK5T+oah+3WepGnPV3sgFDgdPh+rwuJ2klcRYBCgI5+eeul/eBHTgJ4ZA7/i/UcV2DkwBeUNVXRSQGpxOmHM7Y0uHuP8hQtttdnAy64QyHuj4cJaiLiUhVnHbWJ1T13XDHA+fv4++Nc1dVV1X9JYyx3IBTMh/sDmeLwhmNUQZ4WVUPhSu2y0maSJwJROR5nHGbQ0WkB04vdQdV3RrK5HRRTB2A54HnVPULce5p7gKsDHX186K4PJMMLuZWOU+q6qZwxwLnm15uBhaH83fmE09rnOaCF3ySZ+ZQjmu93KWZNk7Xz8BAt6PjBuBuVd0K4bvVTFW/E5EzOHe6pFfVj3FmHwq3K3CGrdzkhWTgy0tJHEBVT4jIR17paFHVKSJyDhghImdVdRzO+F+TStJaiTMbTg9sO5yV76aEOaTz3Eb9l4AWwC4N8X3fiQlXKdykDrcpaFMoxiCnNWkqcSYQkRh3aIanEoOI5A3H8BVjTGDSauL0VMI0xkSWNJk4jTHmv0hTdw4ZY0xqsMRpjDEBssRpjDEBssSZRohIvIisFpFfRORrd9D2pZ6rqYhMdp+3S5gsJYljc7izLQV6jadE5AF/t190zEci0imAaxUXEU+NDTXeZokz7TipqtVUtTLOCpWDfHeKI+Dvg6pOVNWXkjkkB85sPMZcNixxpk3zgdJuSesPEfkE+AVnKYVrRWSRiKx0S6ZZAESkpYj8LiIrce66wt3eS9zllEUkv4h8KyJr3Ed9nEH9pdzS7qvucf8TkWUislZEnvY516Misl6c2e/LpfRDiEh/9zxrRGT8RaXoFiKy3D1fG/f4aBF51efaA5M4tTHJssSZxrgTibTCuf0UnMkf3lXVSjgrRj4GtFDVGsByYLA78cdInDkxawIFkjj9W8CPqloVqIEzRd5DOHevVFPV/4nIte41awPVgJoi0tid+aiLu+16nMmKU/KNqtZyr7cOZ6rABMXda7QGhrs/Q1/gsDuBdS2gvzjrThkTkLR2r3pallFEVrvP5+PMDF4I+EtVF7vb6+IsKrZAnMUO0+NMU1Ye2Jwwh6OIfIozM/3FmuGsUYQ70fJhEcl50THXuo9V7ussOIk0K/BtwnRsIjLRj5+psog8h9MckAVndcsEX7m3rW4QZ02p8u51q/i0f2Z3r73ej2sZc54lzrTjpKpW893gJsfjvpuA71W160XHXfC+/0iAF/WidZPEWbkyUB/hzG61RpyZzZv67Lv4zg51r32XqvomWESk+CVc26RhVlU3vhYDDcRZYgQRySzO2jW/A8VFpJR7XNck3j8LZxLkhPbE7Diz8mT1OWYG0Men7bSwiOQD5gEdRCSjOMuKtPUj3qzATne2q24X7essIlFuzCVxZv2fAdzmHo+IlBWRzH5cx5gLWInTnKeqe92S2xfizB4O8JiqrhdnZvgpInICp6qfNZFT3IMzlVlfnMXAblPVRSKywB3uM81t56wALHJLvMdwlr5YKSJjcZYS2QMs8yPkx4ElwF73/74x/Y0zw382YJCqnhKRD3DaPleKc/G9OKudGhMQu1fdGGMCZFV1Y4wJkCVOY4wJkCVOY4wJkDDlF5cAAAAeSURBVCVOY4wJkCVOY4wJkCVOY4wJkCVOY4wJ0P8DQBs2BEeQP0kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_classifier = RandomForestClassifier(n_estimators=600, min_samples_split=25)\n",
    "rf_classifier.fit(combined_x_train, y_train)\n",
    "\n",
    "# Predict\n",
    "pred_probs = rf_classifier.predict_proba(combined_x_test)\n",
    "\n",
    "# Results\n",
    "display_results(y_test, pred_probs)\n",
    "\n",
    "with open('pred_probas/combined_rf_classifier.pkl', 'wb') as f:\n",
    "    pickle.dump(pred_probs, f)\n",
    "\n",
    "with open('trained_models/combined/RF.pkl', 'wb') as f:\n",
    "    pickle.dump(rf_classifier, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy =  0.636\n",
      "Test Set F-score =  0.641\n",
      "Test Set Precision =  0.706\n",
      "Test Set Recall =  0.628\n",
      "Confusion matrix, without normalization\n",
      "[[ 98   9 106   1   2   8]\n",
      " [ 22 121 122   2   8  25]\n",
      " [ 28  27 485   1   7  35]\n",
      " [  0   0  13 234   0   0]\n",
      " [  1   0  26   0 214   0]\n",
      " [ 16  15 229   3   8  94]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_classifier = xgb.XGBClassifier(max_depth=7, learning_rate=0.008, objective='multi:softprob', \n",
    "                                   n_estimators=600, sub_sample=0.8, num_class=len(emotion_dict),\n",
    "                                   booster='gbtree', n_jobs=4)\n",
    "xgb_classifier.fit(combined_x_train, y_train)\n",
    "\n",
    "# Predict\n",
    "pred_probs = xgb_classifier.predict_proba(combined_x_test)\n",
    "\n",
    "# Results\n",
    "display_results(y_test, pred_probs)\n",
    "\n",
    "with open('pred_probas/combined_xgb_classifier.pkl', 'wb') as f:\n",
    "    pickle.dump(pred_probs, f)\n",
    "\n",
    "with open('trained_models/combined/XGB.pkl', 'wb') as f:\n",
    "    pickle.dump(xgb_classifier, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy =  0.649\n",
      "Test Set F-score =  0.662\n",
      "Test Set Precision =  0.664\n",
      "Test Set Recall =  0.666\n",
      "Confusion matrix, without normalization\n",
      "[[125   7  64   7   5  16]\n",
      " [  8 190  36   8  11  47]\n",
      " [ 42  33 392  16  24  76]\n",
      " [  0   0  15 232   0   0]\n",
      " [  1   3  25  14 198   0]\n",
      " [ 20  48 135  14  12 136]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc_classifier = LinearSVC()\n",
    "\n",
    "svc_classifier.fit(combined_x_train, y_train)\n",
    "\n",
    "# Predict\n",
    "pred = svc_classifier.predict(combined_x_test)\n",
    "\n",
    "# Results\n",
    "one_hot_true = one_hot_encoder(y_test, len(pred), len(emotion_dict))\n",
    "print('Test Set Accuracy =  {0:.3f}'.format(accuracy_score(y_test, pred)))\n",
    "print('Test Set F-score =  {0:.3f}'.format(f1_score(y_test, pred, average='macro')))\n",
    "print('Test Set Precision =  {0:.3f}'.format(precision_score(y_test, pred, average='macro')))\n",
    "print('Test Set Recall =  {0:.3f}'.format(recall_score(y_test, pred, average='macro')))\n",
    "plot_confusion_matrix(confusion_matrix(y_test, pred), classes=emo_keys)\n",
    "(y_test, pred_probs)\n",
    "\n",
    "with open('pred_probas/combined_svc_classifier_model.pkl', 'wb') as f:\n",
    "    pickle.dump(svc_classifier, f)\n",
    "    \n",
    "with open('trained_models/combined/SVC.pkl', 'wb') as f:\n",
    "    pickle.dump(svc_classifier, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy =  0.607\n",
      "Test Set F-score =  0.608\n",
      "Test Set Precision =  0.719\n",
      "Test Set Recall =  0.574\n",
      "Confusion matrix, without normalization\n",
      "[[ 88  13 107   3   5   8]\n",
      " [  4 166  94   3   8  25]\n",
      " [ 18  20 509   3   9  24]\n",
      " [  0   0  70 177   0   0]\n",
      " [  0   8  74   0 159   0]\n",
      " [  4  30 224   9   7  91]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU4AAAEmCAYAAAAN9HleAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xd4FGXXwOHfSYIUadJCCCBK7y00RUEQAekC0kRAFMSO+tkLit3XAoKivnYUsLwq0rsU6QICogKCQgid0BIkCef7Yya4QMouZps5t9dc7JSdOcmuJ0+bZ0RVMcYY472IYAdgjDHhxhKnMcb4yBKnMcb4yBKnMcb4yBKnMcb4yBKnMcb4yBKnOU1E8ovIdyJyWES++Afn6Scis3IytmARkStE5Ndgx2FCi9g4zvAjIn2Be4FqwFFgLfCsqi7+h+ftD9wJXKaqqf840BAnIgpUVtUtwY7FhBcrcYYZEbkXeB14DogGygNvAl1y4PQXA7/lhqTpDRGJCnYMJkSpqi1hsgBFgGNAzyyOyYuTWHe5y+tAXndfS2AncB+wF0gABrn7ngJOAinuNQYDI4DxHueuACgQ5a4PBH7HKfVuA/p5bF/s8b7LgJXAYfffyzz2LQBGAkvc88wCSmTys6XH/4BH/F2Ba4HfgIPAIx7HNwaWAonusWOAC9x9C92f5bj78/byOP+DwG7gk/Rt7nsqutdo4K6XAfYBLYP93bAlsIuVOMNLMyAf8HUWxzwKNAXqAXVxksdjHvtL4yTgWJzkOFZELlLVJ3FKsZNUtaCqvpdVICJyITAaaK+qhXCS49oMjisGTHWPLQ68CkwVkeIeh/UFBgGlgAuA+7O4dGmc30Es8ATwLnAD0BC4AnhcRC5xj00DhgMlcH53rYHbAFT1SveYuu7PO8nj/MVwSt9DPC+sqltxkup4ESkAfAB8pKoLsojX/AtZ4gwvxYH9mnVVuh/wtKruVdV9OCXJ/h77U9z9Kao6Dae0VfU84zkF1BKR/KqaoKobMzimA7BZVT9R1VRVnQD8AnTyOOYDVf1NVZOBz3GSfmZScNpzU4CJOElxlKoeda//M84fDFR1taouc6+7HXgbaOHFz/Skqv7lxnMGVX0X2AIsB2Jw/lCZXMYSZ3g5AJTIpu2tDPCHx/of7rbT5zgr8SYBBX0NRFWP41RvbwUSRGSqiFTzIp70mGI91nf7EM8BVU1zX6cntj0e+5PT3y8iVURkiojsFpEjOCXqElmcG2Cfqp7I5ph3gVrAG6r6VzbHmn8hS5zhZSnwF067XmZ24VQz05V3t52P40ABj/XSnjtVdaaqtsEpef2Ck1Cyiyc9pvjzjMkXb+HEVVlVCwOPAJLNe7IcZiIiBXHajd8DRrhNESaXscQZRlT1ME673lgR6SoiBUQkj4i0F5GX3MMmAI+JSEkRKeEeP/48L7kWuFJEyotIEeDh9B0iEi0iXdy2zr9wqvynMjjHNKCKiPQVkSgR6QXUAKacZ0y+KAQcAY65peFhZ+3fA1zq4zlHAatU9Wactttx/zhKE3YscYYZVX0FZwznYzg9ujuAO4Bv3EOeAVYBPwHrgR/dbedzrdnAJPdcqzkz2UW4cezC6WluwbmJCVU9AHTE6ck/gNMj3lFV959PTD66H6fj6ShOaXjSWftHAB+JSKKIXJ/dyUSkC9COv3/Oe4EGItIvxyI2YcEGwBtjjI+sxGmMMT6yxGmMMT6yxGmMMT6yxGmMMT4K60kMLipWQsuUKx/sMM6QNyr0/hadSEnL/qAAy5cnMtghmH9gzY+r96tqyZw8Z2Thi1VTz7lZK0OavG+mqrbLyev7IqwTZ5ly5fl82sJgh3GG8sULZH9QgG3ZcyzYIZyjWplCwQ4hQ6E4yORUCAZVKF/k2XeD/WOamkzeqtmOCgPgxNqx2d0BhohsxxmKlgakqmqce8PCJJwJa7YD16vqIRERnDG61+LcvTZQVX/M7NyhVzwyxuRSAhLh3eK9q1S1nqrGuesPAXNVtTIw110HaA9UdpchOHedZcoSpzEmNAgg4t1y/roAH7mvP+Lv25e7AB+rYxlQVERiMjuJJU5jTOiIiPRucSa7WeWxDMngbArMEpHVHvujVTXBfb0bZzJwcCad2eHx3p2cORHNGcK6jdMY828ivlTD93tUvzPTXFXjRaQUMFtEfvHcqarqPj7FZ1biNMaEjhysqqtqvPvvXpzJvxsDe9Kr4O6/e93D44FyHm8vSxYzeFniNMaEBiHHOodE5EIRKZT+GrgG2ABMBga4hw0AvnVfTwZuFEdT4LBHlf4cVlU3xoSIf9zx4yka+NoZZUQU8JmqzhCRlcDnIjIYZ0Lt9PFP03CGIm3BGY40KKuTW+I0xoQO34YaZUpVf8d9hMpZ2w/gPHvq7O0K3O7t+S1xGmNChKT3mIc8S5zGmNCQPo4zDOTKzqGP3x1Dl1aN6Nq6Mf93+yD+OnGCZYsX0LNdc7pfcxn9u7Xhz21bAxrTbUMHc2n50jRpWOf0tpFPPUGzRvW4vEkDunRsS8Ku8310kPdG/N/ttG5YkZ7XND297XDiQYbd0IUuLesz7IYuHDl8CICP3h5F7/bN6d2+OT2vaUrcpRdxOPGg32NMd+LECa64rAlNGtajYd1ajHzqyYBdOyvVq1xCowZ1aNqoPs2bNQp2OACMGf06jerXpnGDOgzq35cTJ7J7Hl2Q5PydQ34R/AgCbE/CLj59fxyTpi7km7krOJWWxvTJXzLy4Xt44Y3/8tWsH+jQ9XreHv1S9ifLQf36D+B/3047Y9vdw+9n6cq1LFn+I+3ad+TF50f6PY5OPfoy5qOvztj2wVuv0fiyFny7YA2NL2vBB2++BsCAoXczcfpiJk5fzB0PPEmDJpdTpGjgnl2WN29eps+ay/LVa1m2ag2zZ81kxfJlAbt+VqbPmseylWtYvHRlsENhV3w848a+wcIfVrDix59IO5XGl59PDHZYGfDLLZd+EfwIgiA1NZW/TiSTmppKcnISJaNjEBGOHz0KwNGjhykZnendVn5xefMruajYmUmncOHCp18nJR1HAlCNadjkcooUueiMbd/PnkbHHn0B6NijLwtmTz3nfTMnf0m7zj38Hp8nEaFgQedJwikpKaSkpIRNVS/QnO+6851PSkoiJqZM9m8KhgjxbgmyXNfGGR1ThoFD7+LqJjXIly8fl13ZmstbtOapl8cw7Mbu5MuXnwsLFeKzyfOCHSoATz/5GBM+/YTCRYowdcbcoMRwYN8+SpZyngxcomQ0B/btO2N/cnISP3w/hweffjngsaWlpXFZkzh+37qFobfeRuPGTQIew9kEoXOHtogIg28ewk03Z3Q3YOCUiY3lruH3UaNyBfLlz0/r1m1o3eaaoMaUofRxnGEgPKLMQYcTDzF/1lRmLl3PvNWbSU4+zndfTeTjd8fy1sdfMXfVr3S9/gZeeurh7E8WAE889QybtvzB9b378va4scEOBxE5p1C3cM506sY1DWg1PV1kZCTLV61h87YdrFq1ko0bNgQ8hrPNmb+IH5av5uvJ03h73JssXhTcqQ8PHTrE1O8ms/6XrWzetpPjSceZ+Nn5PjHaz/w/yUeOyHWJc9niBcSWu5hixUuSJ08eWrfvzJpVy/h10wbqNHAa8tt37s7a1cuDHOmZru/Vl8nf/C8o1y5esiT79u4GYN/e3RQrceb8tbO++1/Aq+lnK1q0KFe2aMnsWTOCGgc4JTyAUqVK0blLV1atXBHUeBbMm8PFFSpQsqTzne/cpRvLly0NakwZE18m+QgqvyZOEfnGnZlkY/rsJCJyTESeFZF1IrJMRKLd7RXd9fUi8oyI+GX23ZgyZflpzUqSk5NQVZYvXkDFytU4duQw23/fDMAPC+dxaaWq/ri8T7Zs2Xz69dQpk6lSJTgxXXl1e6Z8+RkAU778jBZtrj297+iRw6xevpiWHtsCZd++fSQmJgKQnJzMvLlzqFK1WsDj8HT8+HGOum3lx48fZ+6c2dSoWSuoMZUtV56VK5aTlOR85xfMn0fVatWDGlOmwqRzyN9tnDep6kERyQ+sFJGvgAuBZar6qIi8BNwCPIMz+/IoVZ0gIrdmdkI3AQ8BiIktl9lhmarToBFtru3K9e2aExkVRbWadenZbxDRMWUYfssNSEQEhYsUZeQrb57Hj3v+Bt3Yl8WLvufA/v1Uq1ieRx5/klkzprN5829ERERQrnx5Xh+d5dyqOeLhO29i9bLFJB46QLum1bl1+MMMGnYvD94+gG8+/4SY2HK8OPbD08fPnzmFple0In+BC/0e29l2JyRwy+CBnEpL49SpU1zXoyfXdugY8Dg87d2zh97XXwdAWmoq1/fuwzVtg/aEBwAaNW5C127dad40jqioKOrWrcegwbcENaYMhUg13BuifpyWX0RGAN3c1QpAW+B7IJ87pVMvoI2q3iwiB3DmyksVkcLALlUtmNX5a9ZtoPbojOzZozO8F4JPqQjVR2es9mJaN59EFCmneZsN9+rYEzPvy/Hr+8JvJU4RaQlcDTRT1SQRWQDkA1L072yd5s8YjDFhJkxKnP5sLCgCHHKTZjWgaTbHLwO6u697+zEuY0xIsgHwADOAKBHZBLyAkxizcg9wr4j8BFQCDvsxNmNMKAqT4Uh+qyar6l84T447W0GPY74EvnRX44GmbttnbyD43drGmMARgYjwaLkLpSgbAmPc5xsnAjcFOR5jTKCFQGnSGyGTOFV1ERlMPGqMyUVCoP3SGyGTOI0xxkqcxhjjC/Hp8cBBZYnTGBM6rMRpjDG+CcScsznBEqcxJiQ4NXVLnMYY4wOxEqcxxvjKEqcxxvjIEqcxxvjIEqcxxvhC3CUMhHXizBsVwSUlAz/zeFZmbNod7BDOUbl4aE4aHIpCcdLgk6mngh1CQAhCRIQNgDfGGJ9YVd0YY3xkidMYY3xhbZzGGOM7K3EaY4wPxO4cMsYY31niNMYYX4TRJB/hMWjKGJMriIhXi5fnihSRNSIyxV2/RESWi8gWEZkkIhe42/O661vc/RWyO7clTmNMyMjJxAncDWzyWH8ReE1VKwGHgMHu9sHAIXf7a+5xWbLEaYwJCemdQzmROEWkLNAB+K+7LkAr/n4c+UdAV/d1F3cdd39ryeYiljiNMaFDvFyghIis8liGnHWm14EHgPT7VYsDiaqa6q7vBGLd17HADgB3/2H3+ExZ4gTS0tJo1rgB3bt2Ctg1xz45nEFX1eae7ledsX3ahPe4s+sV3H1dSz5+beTp7dt/+5mHb+zE3de1ZHiPVpz864TfYxz/3pt0a92Ybq0b8cl/x56x76O3R1OnXCEOHdzv9zgyc+LECa64rAlNGtajYd1ajHzqyaDF4mnM6NdpVL82jRvUYVD/vpw44f/P6mzxO3fQpf3VNGtYh8vi6vL22NEAvPjs09SsfDEtmjWkRbOGzJ45PeCxZUp8qqrvV9U4j+Wd06cR6QjsVdXV/grVetWBsW+Momq16hw9ciRg12zZuRftew9i9GN3n962fuUSViyYyaufzyHPBXk57CaltNRURj16J3c/M5oKVWtyNPEgkVF5/Brf5l9+5qvPPuSzKQvIk+cChvXvRovW7Sh/SUV279rJ0oXziIkt59cYspM3b16mz5pLwYIFSUlJoXXLK2jbrj2NmzQNWky74uMZN/YNVq7dQP78+bmxXy++/HwiN9w4MKBxREZF8fTzL1G3XgOOHj1K6yua0KLV1QAMu+Nu7rj73oDG460cGo50OdBZRK4F8gGFgVFAURGJckuVZYF49/h4oBywU0SigCLAgawukOtLnPE7dzJj+jQGDhqc/cE5qGbDphQsfNEZ22Z+/jHdBt1BngvyAlCkWAkA1i79ngqVq1Ohak0AChUtRmRkpF/j27blV+rUjyN//gJERUUR16Q5c2ZMBuClpx5i+KMjgz7mTkQoWLAgACkpKaSkpITEUxJTU1NJTk4mNTWVpKQkYmLKBDyG0qVjqFuvAQCFChWictVqJCTsCngcvpII8WrJiqo+rKplVbUC0BuYp6r9gPlAD/ewAcC37uvJ7jru/nmqWU+TlesT5wP3D+fZ518MiemsEv7YyqYfl/PQDR14fPB1bNmw1t3+O4jw9LA+3N/7Gr75YGw2Z/rnKlWtzo8rfiDx0AGSk5NYNH8me3bFM3/mFEqVLkPVGrX9HoM30tLSaBJXn4tjo2nd+moaN24S1HjKxMZy1/D7qFG5ApUqxFKkcBFat7kmqDH9+cd21q9bS8O4xgD89+03uaJJfe4cdjOJhw4FNbaz5XCv+tkeBO4VkS04bZjvudvfA4q72+8FHsruRH7LFiJSQUQ2+Ov8OWH61CmULFmS+g0aBjsUwEkCx44k8vwnU7jxnsd55YGhqCppaan8smYF9zw3hmc/+Ibl82fw0/JFfo3l0srVGHTbcIb268qwG7pRtUYdTp78i3fHvMLt9z3q12v7IjIykuWr1rB52w5WrVrJxg3B/codOnSIqd9NZv0vW9m8bSfHk44z8bPxQYvn2LFjDOx3Pc+++AqFCxdm0M1DWb3+V75fupro6Bgef+T/ghbb2bxNmr4kTlVdoKod3de/q2pjVa2kqj1V9S93+wl3vZK7//fszhv8YlYQLV26hKlTv6N6lUsY0L8P3y+Yx00D+wctnuLRMTRpfS0iQuXa9ZGICI4cOkjx6BhqNGhK4YuKkzd/ARo0b8Xvm9b7PZ7reg9g0rRFfPjVTAoXKUrFKtWJ37Gdnm0vo12zmuxJiKdX+yvYv3eP32PJTtGiRbmyRUtmz5oR1DgWzJvDxRUqULJkSfLkyUPnLt1YvmxpUGJJSUlhYL/r6dGrD526dAOgVHQ0kZGRREREcOOgwfy4alVQYsuMn0ucOcbfiTNSRN4VkY0iMktE8ovILSKyUkTWichXIlIAQEQ+FJFx7tCC39yeMb96+pnn2fz7Djb9to2PPplAi5ateP/DT/x92Uw1vqodG1YuAWDXH1tJTTlJ4YuKUe+ylvyxZRN/JSeRlprKxtVLKXdpFb/Hc2D/PgAS4ncwd8ZkOvfoy/drtzFj6UZmLN1IdEwsk6YvokSpaL/HkpF9+/aRmJgIQHJyMvPmzqFK1WpBiSVd2XLlWbliOUlJSagqC+bPo2q16gGPQ1W567ZbqFK1GrfdOfz09t27E06/nvrdN1SvUTPgsWUlXBKnv3vVKwN9VPUWEfkc6A78T1XfBRCRZ3BG7b/hHl8BaAxUBOaLSCVVPWMshzteawhAufLl/Ry+/7z60DA2rlrK0cSD3HJNQ3oNu49WXXvz5pP3ck/3q4jKk4c7R45yOkAKF6VT/6E80M8pjTZo3oqGV17t9xjvHdKPw4kHiYrKwyPPvErhIkX9fk1f7E5I4JbBAzmVlsapU6e4rkdPru3g97+3WWrUuAldu3WnedM4oqKiqFu3HoMG3xLwOJYvXcLnEz6lRs1atGjmNEU9NuIZvvpiIht+WoeIUP7iCrwy+s2Ax5al4OdEr0g2nUfnf2Lnfs/ZqlrZXX8QyAMsAp4BigIFgZmqequIfAgsVNX33eMXAnep6trMrtGgYZwuXrrSL/GfL3vmkHcqRYfWs6LSpZ2yZw55o3jBPKtVNS4nz5k3urLG9hvl1bHbXuuQ49f3hb9LnH95vE4D8gMfAl1VdZ2IDARaehxz9rc29L7Fxhi/EIEImx0pU4WABBHJA/Q7a19PEYkQkYrApcCvAY/OGBMkOd+r7i/BuHPocWA5sM/917Me+SewAmek/61nt28aY/7dQiAnesVviVNVtwO1PNb/47H7rUzeNkdVb/VXTMaY0BYKpUlv2L3qxpjQIFbi9JmqDgx2DMaY4BHCp3MoZBKnMcZY4jTGGF9YVd0YY3wjWOeQMcb4KDTGaHrDEqcxJmSESd60xGmMCR1W4jTGGF9Y55AxxvjGxnEaY8x5sKq6Mcb4KEzyZngnTlU4mRZak7y2qlwq2CGcI+byu7M/KMAOrngj+4NM7iJW4jTGGJ84A+CDHYV3LHEaY0KEDYA3xhifhUnetMRpjAkRYfTMIUucxpiQYJN8GGPMebDEaYwxPgqTvGmJ0xgTOqzEaYwxvrBJPowxxjdi4ziNMcZ3kWEyHCki2AEE2h1Db6byxTE0i6t7etv6dWtp0+IyrmjSkKsub8LqlSsCGtPOnTvo1L41TRvWpllcHcaNHQ3AoYMH6daxLQ3rVKNbx7YkHjrk91h+mfoUKz9/hGUTH2Lxpw8AcFHhAkx56w7Wf/sEU966g6KF8gNQtFB+Jr1yCysmPcyiT+6nRsUYv8fn6cSJE1xxWROaNKxHw7q1GPnUkwG9fmbeHDOaxg3q0Kh+bca+MSooMcTv3EGX9lfTrGEdLoury9vudyrd2NGvUbxgHg7s3x+U+DIj4t0SbLkucfbpfyNffjP1jG1PPvYQDzzyOIuWr+bhx5/kycceCmhMUZFRPPPcyyxbvZ5Z85fw33fe4pdNP/PaKy9yZctWrP7pF65s2YrXXnkxIPG0GzKKpr1foHm/lwC4f1AbFqz4ldpdnmbBil+5f9A1ADwwuC3rft1J417PM/jxT/jP//UISHzp8ubNy/RZc1m+ei3LVq1h9qyZrFi+LKAxnO3njRv48P3/smDxMpauXMOMaVPZunVLwOOIjIri6edfYunqn5g5fzHvvTuOXzb9DDhJdf7c2ZQtVz7gcWVF3Ek+vFmCLdclzsubX8lFxYqdsU1EOHr0KABHjhyhdEyZgMZUOiaGuvUbAFCoUCGqVK1Gwq54pk/9jj79bgSgT78bmTZlckDjStexZR3Gf7ccgPHfLafTVXUAqHZpab5f+RsAv23fw8VlilGqWKGAxSUiFCxYEICUlBRSUlKCXhz59ZdNxDVqTIECBYiKiqL5FVcy+ZuvAx5H6dIx1K3393eqctVqJCTsAuDRB+9nxDPPh0QCOluEeLcEW6aJU0QKZ7UEMkh/e+6lV3nikQepWbkCTzz8AE88/WzQYvnzj+38tG4tDRs1Ye/ePZSOcaq/0aVLs3fvHr9fX1X57s07WPLpA9x03eUAlCpeiN37jwCwe/8RShV3kuP63+Lp0spp8oireTHlY4oRG13U7zF6SktLo0lcfS6OjaZ166tp3LhJQK9/tuo1a/HDksUcOHCApKQkZs6cTvzOHUGN6c8/trN+3VoaxjVm2pTJxJQpQ63adbN/YxDkRIlTRPKJyAoRWSciG0XkKXf7JSKyXES2iMgkEbnA3Z7XXd/i7q+QXZxZdQ5tBBTnTqh06esK+K2c7wY+RVVr+esant5/922ee+kVOne9jq+/+oK7ht3CN1NnBeLSZzh27Bg39r2e5196lcKFz/zbFKgqSutBr7Fr32FKXlSQKePu4Nftu885RtX59z8fzOY//9eDZRMfYuPmXaz7dSdpAZ4fNTIykuWr1pCYmEjvntexccMGatYKyNcmQ9WqVWf4ff9H147tKFDgQurUqUtkZGTQ4jl27BgD+13Psy++QlRUFK/95wW++nZ60OLJTg59xf8CWqnqMRHJAywWkenAvcBrqjpRRMYBg4G33H8PqWolEekNvAj0yuoCmZY4VbWcqpZ3/y131npoNY78QxM+/ZhOXboB0PW6Hvy4amXAY0hJSWFA35707NXndCylSkWzOyEBgN0JCZQs6f9JknftOwzAvkPHmDzvJxrVrMDeA0cpXcJJ5KVLFGbfQadZ4+jxEwwdMZ6mvV9g8OMfU+KigmyLP+D3GDNStGhRrmzRktmzZgTl+p4GDBrMoqUrmTl3AUWLXkSlylWCEkdKSgoD+11PD/c7tf33rfy5fTtXNmtIvRqV2BW/k6uaN2bPnnP/OAaDAJEiXi1ZUccxdzWPuyjQCvjS3f4R0NV93cVdx93fWrIppXjVxikivUXkEfd1WRFp6OX7LhSRqW6ReYOI9BKRJ0Rkpbv+TnqAItLQPW4dcLs3588pMTFlWLLoewAWLpjHpRUrB/LyqCp3DruFKlWrc/tdw09vb3dtRyZ8+jHgJPf2HTr5NY4C+S6gYIG8p19f3awaG7fuYur367mhk1MFvqFTE6Ys+AmAIgXzkyfKKU0N6nYZi3/cwtHjJ/wao6d9+/aRmJgIQHJyMvPmzqFK1WoBu35m9u3dC8COP/9k8rdf07NXn4DHoKrcddstVKlajdvudL5TNWrV5tftu1j78xbW/ryFMrFlmb94BdHRpQMeX4a8rKa7KaOEiKzyWIaceSqJFJG1wF5gNrAVSFTVVPeQnUCs+zoW2AHg7j8MFM8q1GzHcYrIGJyMfSXwHJAEjAMaefGraAfsUtUO7rmKALNV9Wl3/ROgI/Ad8AFwh6ouFJGXs4hnCDAEOK9ewcED+rFk4fccOLCfmpUu5qHHnuT1seN4+P57SU1LJV/evLw+5i2fz/tPLFu6hEkTxlOjZm2uaOr8TXp8xEiG3/cgg/r3ZvzHH1CuXHk++GSiX+MoVbwQk169BYCoyEgmTV/F7B82sXrjn4x/8SYGdG3GnwkHueGB9wGnc+jdp/ujqmzamsCtT33q1/jOtjshgVsGD+RUWhqnTp3iuh49ubZDx4DGkJF+vXty8OAB8uTJw6uvv0HRooFt9wVYvnQJn0/4lBo1a9GimfOdemzEM7Rp2z7gsfjCh6r6flWNy2ynqqYB9USkKPA1kKN/UUXTG6wyO0DkR1VtICJrVLW+u22dqmbbuiwiVYBZwCScNstFItIdeAAoABQD3sBJxD+lNwGISB3gs+zaOOs3iNP5S5Zn+0MGVNa/zqCwZw55L+1U6H2AJ1ND67laAMUL5lmdVeI6HxdVqKFXPf6JV8d+fXOc19cXkSeAZOBBoLSqpopIM2CEqrYVkZnu66UiEgXsBkpqFsnRm6p6iohE4KYEESkOePVJqupvQANgPfCM+wO8CfRQ1drAu0A+b85ljPn3y4kB8CJS0i1pIiL5gTbAJmA+kD7YeADwrft6sruOu39eVkkTvEucY4GvgJJut/5inF6nbIlIGSBJVccDL+MkUYD9IlIw/YdQ1UQgUUSau/v7eXN+Y8y/Sw4NgI8B5ovIT8BKnObBKTglzntFZAtOG+Z77vHvAcXd7fcC2d4Bk20bp6p+LCKrgasjApYgAAAgAElEQVTdTT1VdUN273PVBl4WkVNACjAMpydrA05x2LP7ehDwvogoTvXeGJOL5NTtlKr6E1A/g+2/A40z2H4C6OnLNbyd5CMSJ/EpPtxtpKozgZlnbV4FPJbBsasBz3bTB7y9jjHm3yG7oUahItskKCKPAhOAMkBZ4DMRedjfgRljcp8cqqr7nTclzhuB+qqaBCAizwJrgOf9GZgxJncRQuM+dG94kzgTzjouyt1mjDE5J0RKk97INHGKyGs4bZoHgY3uWCcFruHMTh1jjMkRYZI3syxxpvecbwQ8J7AM7oSHxph/rbAvcarqe5ntM8aYnPavauMUkYrAs0ANPO7yUdXgTPlijPnXigiTEqc3YzI/xJmAQ4D2wOc4954bY0yOEXESpzdLsHmTOAu4A9lR1a2q+hhOAjXGmBwVLg9r82Y40l/uJB9bReRWIB4I3INljDG5Rth3DnkYDlwI3IXT1lkEuMmfQRljcqcwyZteTfKRPuHlUaC/f8MxxuRWQmi0X3ojqwHwX5PFtLyqep1fIvJBhEC+PMF7EFa4WD8z0wn1g+ab9fHBDiFD3eqUDXYI54iKzCVP8Q6R9ktvZFXiHBOwKIwxhvCZHSmrAfBzAxmIMSZ3E/5dnUPGGBMQ/5o7h4wxJlD+dYlTRPKq6l/+DMYYk3s5g9vDI3N6MwN8YxFZD2x21+uKSGg+29UYE9YixLsl2LwZ5zAa6AgcAFDVdcBV/gzKGJP7CBAZIV4tweZNVT1CVf84qwid5qd4jDG5WLiMWPUmce4QkcaAikgkcCfwm3/DMsbkRmHSxOlV4hyGU10vD+wB5rjbjDEmx0iITBnnDW/uVd8L9A5ALMaYXC5M8qZXvervisg7Zy+BCC4QZs2cQZ2aValZrRIvv/RCsMMBQiOm37f8RqdWTU4v9SpG88HbY0g8dJABPTtyddPaDOjZkcOJh/wey9tP3cetV9fjgetbn942+qFhPNynLQ/3actdHZvxcJ+2ACye9vXp7Q/3aUu/uPJs/3Wj32P0FAqf39lCMaaMhEuvuqhmOo+Hc4BIL4/VfEA3YIeq3unPwLzRsGGcLlm+6rzfn5aWRu0aVZg6fTaxZcvSvGkjPho/geo1auRglMGPaefB5H8cU/O6Ffly+kLGf/A2RYtexNC77uft0f/h8OFEHnj8GZ/PuXLnAa+P3fTjMvLlv5C3nryHlz4/907g8a8+TYGChbluyD1nbP9z8yZeve9mXp+8xOtr/dNJPnLLdyp/HlmtqnE5GCaxVWrr0LFfe3Xsk9dUzvHr+yLbEqeqTvJYPgKuAxr6PzT/W7liBRUrVuKSSy/lggsuoGev3kz57luL6Sw/LJpP+QqXEluuPHNnTKFbr34AdOvVjznTv/P79as3aErBIkUz3KeqLJszhWbtupyz74eZ39KsbWd/h3eGUPz8QjGmDAlERni3BNv5hHAJEJ3TgQTDrl3xlC1b7vR6bGxZ4uODO91ZKMY09esv6NitJwD79+2lVHQMACVLlWb/vr3BDI1f1iynSLESxJS/5Jx9y2Z9x2Vtz02o/hSKn18oxpQZ8fK/YPOmjfOQiBx0l0RgNvCwLxcRkbtEZJOIfHq+gZrgOHnyJPNmTaN9p3OnXxWRoN8i98OMbzNMjlvWryFvvvyUq1QtCFGZ85H+eOBwaOPMslddnP8r6uI8ZwjglGbXKJqx24CrVXXnebzXb8qUiWXnzh2n1+PjdxIbGxvEiEIvpoVzZ1Kjdj1KlHIqGSVKlmLvngRKRcewd08CxUuUDFpsaamprJw/g2fHTztn39JZ32ZYffe3UPv8IDRjykwoJEVvZFnidJPkNFVNcxefk6aIjAMuBaaLyKMi8r6IrBCRNSLSxT2mgogsEpEf3eWy8/ppfBTXqBFbtmxm+7ZtnDx5ki8mTaRDx8C2iYV6TFM8qukArdp24OtJTsXh60mf0rpdx2CFxoYViyhToSLF3aaDdKdOnWLZ7Ck0uybwv7dQ+/xCNabMpNdisluCzZs2zrUiUv98L6CqtwK7cO5vvxCYp6qN3fWXReRCYC/QRlUbAL1wBtxnSESGiMgqEVm1b/++8w0LgKioKF4bNYZOHdpSr3Z1uve8nho1a/6jc/5ToRRT0vHjLFk4j7Yd/i65Db3zPpZ8P4+rm9bmh4XzGXrnfX6P441HbufJgV1J2P47d7RvxPxvJgKwdObkDKvpv/y4nOLRZYgue7HfYztbKH1+oRxTRsKpqp7pcCQRiVLVVBHZCFQFtgLHcX4+dZOcdxcR2Q7EATNwhjSluruKAW1xEusYoB7OffBVVLVAduf9p8ORcot/OhzJH3wZjhRIofjMoVDkj+FI5arV1uHveNfbf1+LikEdjpRVG+cKoAGQk2V6Abqr6q9nbBQZgXM7Z12cUvCJHLymMSYMCBAVCsVJL2RVVRcAVd2a0XKe15sJ3Ol2OuHRBFAESFDVUziPILZHVxqTC4l4t2R9DiknIvNF5GcR2Sgid7vbi4nIbBHZ7P57kbtdRGS0iGwRkZ9EJNvadFYlzpIicm9mO1X11exOnoGRwOvATyISAWzDmevzTeArEbkRpzp//DzObYwJa0JEzozRTAXuU9UfRaQQsFpEZgMDgbmq+oKIPAQ8BDwItAcqu0sT4C3330xllTgjgYLwz38SVa3gsTo0g/2bgToemx78p9c0xoQX5ymX//w8qpoAJLivj4rIJiAW6AK0dA/7CFiAk2u6AB+7o4aWiUhREYlxz5OhrBJngqo+/Y9/CmOM8YZvPeYlRMSzZ/gdVT1n8iERqQDUB5YD0R7JcDd/3wEZC+zweNtOd9t5Jc7waKU1xvxr+DAf5/7setVFpCDwFXCPqh7xHP+pqioi53MzjxNnFvtaZ7HPGGNyVHpV/Z92DgGISB6cpPmpqv7P3bxHRGLc/TE448fBuTOynMfby/L33ZIZyjRxqurB7MMzxpickxMPa3NH7bwHbDqrE3syMMB9PQD41mP7jW7velPgcFbtm+DDc9WNMcafhBx7WNvlOMMa14vIWnfbI8ALwOciMhj4A7je3TcNuBbYAiQBg7K7gCVOY0xoEHLkPnRVXUzmfTTnNEG6vem3+3INS5zGmJARLj3SljiNMSHBmeQjPFKnJU5jTMgIj7RpidMYEzKEiDCZ5MMSpzEmJORgr7rfWeI0xoSMUJjd3RuWOI0xISM80qYlzhyXmnYq2CGc4/yer+dfoTrT+uxNe4IdwjnaVP9XPI07ezk0jjMQLHEaY0KCtXEaY8x5sBKnMcb4KExGI1niNMaEBqeqHh6Z0xKnMSZkhElN3RKnMSZUCGIlTmOM8Y2VOI0xxgfWxmmMMb7y8nlCocASpzEmZITLfJzhMlDfb2bNnEGdmlWpWa0SL7/0QrDDAWDM6NdpVL82jRvUYVD/vpw4cSLgMfy+5Tc6t256eqlfqTQfvjPm9P733hpFldIXcvDA/oDH5ilYn9+oJ+6hf4ua3NGtxeltn735MgOvrsfdPVtzd8/WrFo0B4CUlJOMevxu7ryuJXf1aMX6lUsCFme6UPyen82ZyNi7JdhydeJMS0vjnrtu59vvprPmp5/5YuIENv38c1Bj2hUfz7ixb7DwhxWs+PEn0k6l8eXnEwMex6WVqjB57jImz13G17OWkD9/ftq07wxAQvxOlnw/lzKx5bI5i38F8/Nr3bkXI96acM72LjcMYdQXcxn1xVzirrgagFlfjQfgjf8t4Om3J/H+f57i1KnAzWkQit/zzIiX/wVbrk6cK1esoGLFSlxy6aVccMEF9OzVmynffZv9G/0sNTWV5ORkUlNTSUpKIiamTFDjWbpoPuUrXEpsufIAPPfEg/zf488E/fa4YH5+teKaUbBIUa+O3bH1N+o0bg5A0eIlubBQYbZsXJvNu3JOqH7PM5JTz1X3t1ydOHftiqds2b9LTbGxZYmPz/I59H5XJjaWu4bfR43KFahUIZYihYvQus01QY1p6jdf0qFrTwDmzJhCdEwM1WvWCWpMEJqf39SJ73Nn96sY9cQ9HDuSCECFqjVZvmAmaamp7N75B1s3/cT+3bsCFlMo/p4yYyVOc14OHTrE1O8ms/6XrWzetpPjSceZ+Nn4oMVz8uRJ5s6aRvvO3UhOSmLcqJe5+4HHgxZPKGvfayBvT13OqC/mUqxENO/9ZwQAbbr2oUR0Ge7t05b/vvQE1erGEREZGdxgQ5C1ceYQEfFrr3+ZMrHs3Lnj9Hp8/E5iY2P9eclsLZg3h4srVKBkyZLkyZOHzl26sXzZ0qDFs3DeLGrWrkuJktH8+cfv7PxzO51bNeWquOrsToin2zWXs2/v7qDEFmqf30XFSxIZGUlERATXdO/H5vVrAIiMiuLmB55m1BdzeWz0Rxw/eoQyF18asLhC7feUOW/Lm8HPnAFJnCJyoYhMFZF1IrJBRHqJyHYRKeHujxORBe7rESLyiYgsAT7xZ1xxjRqxZctmtm/bxsmTJ/li0kQ6dOzsz0tmq2y58qxcsZykpCRUlQXz51G1WvWgxTPl6y/o6FbTq1avxbKNfzB/1Sbmr9pE6ZhYvp61hJKlSgcltlD7/A7u+3sS5GXzpnNx5WoA/JWcxImk4wCsWfo9EZFRlK9YNWBxhdrvKVNeljZDocQZqHGc7YBdqtoBQESKAC9mcXwNoLmqJvszqKioKF4bNYZOHdqSlpbGgIE3UaNmTX9eMluNGjeha7fuNG8aR1RUFHXr1mPQ4FuCEkvS8eP8sHAeI18eHZTrZyeYn9/LD9zKhlU/cCTxIIOurk+f2/6PDat+YNsvG0CE6DLluO2JlwFIPLifEbf2QSIiKF6qNPc+90ZAYkwXit/zjITTc9UlEI9VEJEqwCxgEjBFVReJyHYgTlX3i0gc8B9VbSkiIwBV1acyOdcQYAhAufLlG/629Q+/x++LUHx0RkJi4MeBZqdc8QLBDiFD9ugM7+TPI6tVNS4nz1m9dn394Ov5Xh3brPJFOX59XwSkqq6qvwENgPXAMyLyBJDqcf18Z73leBbnekdV41Q1rmSJkn6J1xgTJOLlEmSBauMsAySp6njgZZwkuh1o6B7SPRBxGGNCW7h0DgWqjbM28LKInAJSgGFAfuA9ERkJLAhQHMaYEBYmTZyBSZyqOhOYmcGuKhkcO8LvARljQpIlTmOM8YHTfBkemdMSpzEmNITIfejesMRpjAkZYZI3Q/uWS2NMLpNDw5FE5H0R2SsiGzy2FROR2SKy2f33Ine7iMhoEdkiIj+JSIPszm+J0xgTInL0XvUPce5Y9PQQMFdVKwNz3XWA9kBldxkCvJXdyS1xGmNCRk7Nx6mqC4GDZ23uAnzkvv4I6Oqx/WN1LAOKikhMVue3xGmMCQmC3ycyjlbVBPf1biD9XtZYYIfHcTvdbZmyziFjTMjwYThSCRFZ5bH+jqq+4+2bVVVF5Lwn6rDEaYwJGT6UJvefxyQfe0QkRlUT3Kr4Xnd7POD5AK2y7rZMWVXdGBMy/DzHx2RggPt6APCtx/Yb3d71psBhjyp9hqzEaYwJDTk485GITABa4lTpdwJPAi8An4vIYOAP4Hr38GnAtcAWIAkYlN35LXEaY0JGTt1yqap9MtnVOoNjFbjdl/Nb4jTGhIT0XvVwENaJU4FTp/w/g70vTqSE3gzwk9YH7lG03rrnisA9rMwXraqWCnYI59i651iwQwgYS5zGGOMjmx3JGGN8ZCVOY4zxUZjkTUucxpgQEiaZ0xKnMSYk2AzwxhjjK5sB3hhjfGeJ0xhjfBIaz0z3hiVOY0zIsBKnMcb4IAfn+PA7S5zGmNARJpnT5uME0tLSaNa4Ad27dgrK9U+cOME1LZvRslkDmjeqy4vPPgXAH9u30faqy2hUtxo3D+jLyZMn/RrH4b0JvH//DYwe3I7RN7dn6f8+BGDGOy8w6qa2jBnSkc9G3EbysSNnvC9x7y5GdqrL4i/+69f4MvLmmNE0blCHRvVrM/aNUQG//tl++/VXmjWqf3qJKVGEsaNfD0osn/z3Tbq2bkyXVo345L9jAZg55Wu6tGpE7XKF2bDux6DElZUcfFibX1niBMa+MYqq1aoH7fp58+blf1Nms2Dpj8z/YRXz5sxk1YplPP3EI9x6+92sXPcLRYsW5dOP3/drHBGRkbQb+jB3vTeDoaO/YPnkT9n7x2YqNbicO96dyh3vTKF4bAUWThh3xvumj3uOyo2u9GtsGfl54wY+fP+/LFi8jKUr1zBj2lS2bt0S8Dg8ValalaUr17B05RoWL1tF/gIF6NSlW8Dj2PzLz3w14UMmTFnAV7OW8v2cGfy5bSuVqlbn9Xc/pWGTywMekzcixLsl2HJ94ozfuZMZ06cxcNDgoMUgIhQsWBCAlJQUUlJSEBEWfz+fTl27A9Crb3+mTZns1zgKFS9Fmco1AchboCAly1fkyP49VIq7gshIp1WnXPV6HNm/+/R7fl4ym4tKl6VUhcp+jS0jv/6yibhGjSlQoABRUVE0v+JKJn/zdcDjyMyCeXO59NKKlL/44oBf+/ctv1K7Xhz58zu/m7imzZkzfTIVK1fjkopVAh6PV7x8UFsodCDl+sT5wP3Defb5F4mICO6vIi0tjZaXNaT6pWVoedXVVLikIoWLFiUqyklYZWLLsntX4KaHO7R7JwlbfqZstbpnbP9x5pdUbtQCgL+Sj7N40jtc1f/OgMXlqXrNWvywZDEHDhwgKSmJmTOnE79zR/ZvDJAvv5hIj+t7B+XalapW58cVP5B46ADJyUksmjeT3buyfIxOiPDzwzNySK7uHJo+dQolS5akfoOGLPx+QVBjiYyMZMEPqzmcmMiAvj3Y/NsvQYvlr+TjTHz6DtoPe5R8FxY6vX3Bp28SERlF3dadAZj/8Rs06z6IvPkvDEqc1apVZ/h9/0fXju0oUOBC6tSpS2RkZFBiOdvJkyeZOuU7Rox8PijXr1i5GjfdNpwhfbuSv0ABqtasQ0SI/G4yYxMZh4mlS5cwdep3zJw5nRMnTnD0yBFuGtif9z/8JGgxFSlalOZXtmTViuUcSUwkNTWVqKgodsXvpHSZMn6/flpqChOfuoM6rTpT84q2p7f/OPMrfls+n4EvfYy43+6dv6xj46IZzHr3JU4cO4JERBCVJy9Nu/b3e5zpBgwazAC3mWXE448SW7ZswK6dlVkzplOvXgOio6OzP9hPuvcZQPc+zrPJXn9hBKVjsnxUeEgIk7zpv6q6iFQQkU0i8q6IbBSRWSKSX0QqisgMEVktIotEpJp7/Ici0sPj/X6f9vrpZ55n8+872PTbNj76ZAItWrYKStLcv28fhxMTAUhOTmbBvDlUqVqNy69syXfffAXApM8+oX0H//b6qypfv/IIJctX5PIeN53evnnlQhZ//i79nh7HBfnyn95+82sTuG/8Au4bv4Bm1w3kyj63BjRpAuzb6zzhdceffzL526/p2SuzR80E1hefT6Rnr+BU09Md2L8PgIT4HcydPplru/YMajzeCJc2Tn+XOCsDfVT1FhH5HOiO8wS5W1V1s4g0Ad4EWnl7QhEZAgwBKFe+vB9CDrw9exK4Y+hNnEpL49Qppct1PbimfQeqVKvOkEH9eG7kk9SuU49+N96U/cn+gT83rmbdnG+IvqQqY4c6SbrNTfcx9c2RpKac5MMHBwJOB1Hne0b6NRZv9evdk4MHD5AnTx5eff0NihYtGuyQOH78OPPnzmb02HHZH+xHw4f0I/HQQaKi8vDos69SuEhR5kyfzPOP/x8HD+7ntgE9qFazDu98+k1Q4/QUCkONvCHOA978cGKRCsBsVa3srj8I5AEeBX71ODSvqlYXkQ+BKar6pXv8MVUtmNU1GjSM08VLV/oh+vOXdDIt2CGcY9yy7cEO4Ryh+swhCYXizFm27zse7BDOUatsodWqGpeT56xbv6HO+n6ZV8eWLnJBjl/fF/4ucf7l8ToNiAYSVbVeBsem4jYdiEgEcIGfYzPGhJBQqYZ7I9BjcI4A20SkJ4A40se7bAcauq8745ROjTG5iN05lLl+wGARWQdsBLq4298FWrjbmwGhVz8xxvhXeAzj9F9VXVW3A7U81v/jsbtdBsfvAZp6bHrQX7EZY0JTCOREr+TqcZzGmNASLm2cljiNMSEiNNovvWGJ0xgTEuyWS2OMOQ+WOI0xxkdWVTfGGF+E0QB4S5zGmJAQIkM0vWKJ0xgTOsIkc1riNMaEjHBp48z1j84wxoSOnHpYm4i0E5FfRWSLiDyU43Hm9AmNMea85cC96iISCYwF2gM1gD4iUiMnw7TEaYwJGTk0O1JjYIuq/q6qJ4GJ/D2ZUM7E6a+JjANBRPYBf+TQ6UoA+3PoXDnFYvKOxeSdnIzpYlUtmUPnAkBEZuDE6I18wAmP9XdU9R33PD2Adqp6s7veH2iiqnfkVKxh3TmUkx+ciKwK5ozSGbGYvGMxeScUY/KkqufMmhaqrKpujPm3iQfKeayXdbflGEucxph/m5VAZRG5REQuAHoDk3PyAmFdVc9h7wQ7gAxYTN6xmLwTijHlOFVNFZE7gJlAJPC+qm7MyWuEdeeQMcYEg1XVjTHGR5Y4jTHGR5Y4zXkREfvumFzLvvzGJyJymYg0UNVTljy9JxIuM00ab9gX/yzpX3D7omcqDvhcROqGQvIM9c9JRJoCaAj3wob67zAUWeI8VxVwvuih8IUKhRjg76q5qo4GPgXeE5GawUyeIiLpCUlEiohIkWDEkRkRGQg8JSLFgh3L2USkn4iMh9D5rocTS5weRKQysFJExkBofKHcGNqIyHARuS+IcZwCcMfHlQJOAh+LSP1gJU+PpHkv8CFOSbhnoOPIiIg0B64H7lfVg+6MPaFkMtBcRN6E0PiuhxNLnC4R6Qw8A7wJdBORcRD8L5SINAPeBpKBXiLyhoiUdfcFNC4RaQzcg/N76gt8ALwvIrWClTxFZBjQCbgBOARMEJGbAx1HBmoC0UB3EblAVdNCITGJSGURiVHVo0BtoLWIvAfB/66HE0ucgIhcCNwLfKaqDwG1gKtEZDQE7wslIrWBQcALqjoOuBIoDDyVHpefr392e28KsFxV44E/gfeAzcA3IlIjvVQaKG5cx3FuqRvqbm4PvCkiNwUyFo+YrhWREar6NvAqzmw/14lIZDATkziqAC8CbUQk2k2ecUAXEXkfQrstNpRY4nScAH4HdgKo6iHgbmCQiIx0twXsC+XxP1dNnIlYm4hIrKqeAG4FaohIrL9j8PiZ87n/bgbqisijqnpKVZOB1cACnBKxX+PxLNG6pThV1Y9xvsftgQdVdTbOrXYjRaRQEBLVXqCpiDygqp8CPwLNgP7pyTPA8QDO91dVfwPeBa4BWnmUPMe666WsxOmdXH2vuohcAuxV1eMishEYLyINVTUJOIZzb+81IjJbVRcGIJ70ZBUL7FTViSKyF7gZuFpEFgCF3MWvpTuP9sOhOG1hK3HaxToDX7nNBX/glPauVdVd/owHuFBVj7kx3QNUFJESwKM4c0xuBxqLSAdgGzDUTQoBISLFVPUgsAZ4GBjhJs+XROR2nD+AFwJHAhWTR2x3ABWBgsDjOHOo9wTKiUh+nA7Rpqq6N9CxhatcmzhFpC3OX9/vReR3YARQDPhBRGbhtOF1BtLcxe/cqlwH4BERWYyTEF4HLgDuAgYCR4GHVTXB3/GIyC3AjcBwnN9VHE57axvgNpySaH9/J023/bkLMFhEbsD5XDoCvwBDVPUhEfkZpymjJdA3AIncM76rgA9EpKuqrhWR9cBzwOsigps8i6hqMJLmMKArMAT4H/CQqt4jIorTJNUI5/u0O9CxhbNcOcmHiDTC+TJNdzd1wklO9+NMu18C+BWncf8N4DpV/T0AcTXH7ZwC7gOaAIuAh9y47gZmue1n/ri+5/CeajgdLv8B+gG9gDk4/7ONUtUl/oghg5iKA5OAO4AknLbo8Ti/m45AV1X9y+P4Iqp6OBCxude7DacUWQvoAPRT1Z/cfR/gjEDo75ZGAyb9sxSRJ3Gq4gOAVsB1OLWVCFX9S0TyqGpKIGP7N8h1iVNE8uK01e1R1UbutoZAD6A48ISq7haRmjidH0NVdZ0f44lU1TT3dRfgN6ACTs/1kzglhT9wkmdboD/wGfC/9PflUByeSfM2nHbD73BKlaNUtZ2IlAHmAt8Az6ZXnf1JRAoBX+D0mOcFNuGUko4BvVQ1xU0OqqpPn9U26+/YhgK3AF1UNV5EHsH5o3cvUAe4ChimqvsCEc9ZsVXBabd/D7gY2A3c4DHlWhpO7UGtQ8h3uapzSEQq4bQPXgmUF/exoaq6GicZHMJJnuB0FHXwV9J0EwLuMJWrRGQQkOAubYGbVHUKcNCNqayq/g+ntLUkJ5OmG4dnm+Yg4FtV/QMogvO7ygM0xKkevxaIpOnGdRSYh1MrWIrTdFEep9pZQkR64ySrLzx/Dn9z2wbb47Sxpri/tzxAAZw/wtcDI4OUNO8ApuL0oG/DGXa0wE2aA3GaWea4HXyWNM9DrmnjFJFOOKW4P3Cq4YOBD0XklKq+pKrLRWRTejuUP6t7IlIAmCrOcKd1OFWpn4HmwEacXth4cWavrg4MVtVf3bi+8mNc6cngMSBJRG7Faa4oi5O8CuNUOwPdiTAJp3d6DM4jEG7Bqbp3xEnsN6jqpkAGpKrJIjINeAHYgfMH5XecDrTngRR31EFAue3BdYB2OL3nhd2YHhSRWkB9oIeqbg50bP8muaKqLs79wm/gtEG1wektfwVYAXwJPKWqzwc4pm441e+DOA3260SkL041vQxwBbAVmKCqXwQwriHAMM5MBtE4//PFB7Pn1W1SmYTTM/w5To2pQCDbNM+KJx9OaW6rOncH9cVJ6h3ckRmBjicWp1Q+R1VvcpuluuM8f6cwMAr4K1i/r3+T3JI4ywIxwEX8fdfL28AunISQ6I7/C3RcbXASwHOq+rKIROF0wlTFGVEBYIwAAASqSURBVFs6zv0fMpDtdmcng344w6GuDUYJ6mwiUhennfUJVX0z2PHA6fv4B+HcVdVHVTcEMZbrcErm97rD2SJwRmNUBl5U1cRgxfb/7d1fiFVVFMfx769CkplRe+gP9TJklJHUoBhREBLDUFkyRH+UICIR9aGCSAgyKNASfKqHiOphikD6a0hDSPRQIjOh3JwSsgmJ8qFonoRyIqrVw1p3OB4cvUeHe29z1gcGZ87dc/a+jCz22XfvteaTWgTOJkk78H2bL0t6BP+UetjMjrczOJXGNAzsALab2W75meZ1QKPdj5+lcXVNMCiLR85pMzvW6bHAzNLLQ8B4J/9mhfGswZcLXiwEz5527mud72qzxhm+BTbFBx33AU+Y2XHo3FEzM/tY0l/4SZcFZvYWnn2o0y7Gt6082A3BoKibgjiAmZ2UNNItH7SY2aikf4HXJf1tZh/g+3/THKnbjHMR/gnsWrzy3WiHhzQjFvV3AoPAr9bmc9+n06lZeJobsRR0rB17kOumVoGzSdJFsTWjqwKDpEs7sX0lpVRNXQNnVwXMlNL/Sy0DZ0opnY9anRxKKaW5kIEzpZQqysCZUkoVZeCsCUn/SDos6Yik92PT9rnea7WkT+L7tc1kKbO0XRLZlqr28bykp1u9XmozIun+Cn31S+qqvaGpu2XgrI9pMxsws+V4hcrNxRflKv9/MLO9ZrbzDE2W4Nl4Upo3MnDW037gmphpfS/pbeAIXkphSNKYpEbMTHsBJN0p6aikBn7qirj+qKKcsqTLJe2RNBFft+Kb+pfGbHdXtNsq6aCkbyS9ULjXs5Im5dnvrzvbm5C0Me4zIenD0ix6UNKhuN890f5CSbsKfW+a5dYpnVEGzpqJRCJ34cdPwZM/vGpmN+AVI7cBg2a2AjgEPBWJP97Ac2KuBK6Y5favAF+Y2U3ACjxF3jP46ZUBM9sqaSj6vBkYAFZKuj0yH62La3fjyYrP5iMzWxX9fYenCmzqjz7WAK/Fe9gAnIgE1quAjfK6UylVUrez6nW2UNLh+H4/nhn8SuAnMxuP67fgRcUOyIsdLsDTlC0DfmzmcJT0Dp6ZvuwOvEYRkWj5hKRLSm2G4uvr+LkXD6R9wJ5mOjZJe1t4T8slbceXA3rx6pZN78Wx1R/kNaWWRb83FtY/F0ffky30ldKMDJz1MW1mA8ULERz/KF4CPjOz9aV2p/zeeRLwkpXqJskrV1Y1gme3mpBnNl9deK18ssOi78fNrBhgkdR/Dn2nGstH9VQ0DtwmLzGCpB557ZqjQL+kpdFu/Sy//zmeBLm5nrgYz8rTV2izD3issHZ6laTLgC+BYUkL5WVF7m1hvH3AL5Ht6uHSaw9IuiDGfDWe9X8fsCXaI+laST0t9JPSKXLGmWaY2VTM3HbLs4cDbDOzSXlm+FFJJ/FH/b7T3OJJPJXZBrwY2BYzG5N0ILb7fBrrnNcDYzHj/R0vfdGQ9C5eSuQ34GALQ34O+AqYin+LY/oZz/C/CNhsZn9KehNf+2zIO5/Cq52mVEmeVU8ppYryUT2llCrKwJlSShVl4EwppYoycKaUUkUZOFNKqaIMnCmlVFEGzpRSqug/fqyA48Hy+ZAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnb_classifier = MultinomialNB()\n",
    "\n",
    "mnb_classifier.fit(combined_x_train, y_train)\n",
    "\n",
    "# Predict\n",
    "pred_probs = mnb_classifier.predict_proba(combined_x_test)\n",
    "\n",
    "# Results\n",
    "display_results(y_test, pred_probs)\n",
    "\n",
    "with open('pred_probas/combined_mnb_classifier.pkl', 'wb') as f:\n",
    "    pickle.dump(pred_probs, f)\n",
    "    \n",
    "with open('trained_models/combined/MNB.pkl', 'wb') as f:\n",
    "    pickle.dump(mnb_classifier, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy =  0.665\n",
      "Test Set F-score =  0.681\n",
      "Test Set Precision =  0.676\n",
      "Test Set Recall =  0.690\n",
      "Confusion matrix, without normalization\n",
      "[[120  17  63   3   3  18]\n",
      " [ 13 182  35   6  12  52]\n",
      " [ 50  42 381   9  17  84]\n",
      " [  0   0   0 247   0   0]\n",
      " [  1   0  10   3 227   0]\n",
      " [ 22  57 126   5   8 147]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlp_classifier = MLPClassifier(hidden_layer_sizes=(500, ), activation='relu', solver='adam', alpha=0.0001,\n",
    "                               batch_size='auto', learning_rate='adaptive', learning_rate_init=0.01,\n",
    "                               power_t=0.5, max_iter=1000, shuffle=True, random_state=None, tol=0.0001,\n",
    "                               verbose=False, warm_start=True, momentum=0.8, nesterovs_momentum=True,\n",
    "                               early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999,\n",
    "                               epsilon=1e-08)\n",
    "\n",
    "mlp_classifier.fit(combined_x_train, y_train)\n",
    "\n",
    "# Predict\n",
    "pred_probs = mlp_classifier.predict_proba(combined_x_test)\n",
    "\n",
    "# Results\n",
    "display_results(y_test, pred_probs)\n",
    "\n",
    "with open('pred_probas/combined_mlp_classifier.pkl', 'wb') as f:\n",
    "    pickle.dump(pred_probs, f)\n",
    "    \n",
    "with open('trained_models/combined/MLP.pkl', 'wb') as f:\n",
    "    pickle.dump(mlp_classifier, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy =  0.631\n",
      "Test Set F-score =  0.643\n",
      "Test Set Precision =  0.686\n",
      "Test Set Recall =  0.620\n",
      "Confusion matrix, without normalization\n",
      "[[119   8  77   1   5  14]\n",
      " [  9 175  59   4  14  39]\n",
      " [ 28  29 450   4  18  54]\n",
      " [ 10   0  53 184   0   0]\n",
      " [  5   7  49   2 173   5]\n",
      " [  7  34 166   9  13 136]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_classifier = LogisticRegression(solver='lbfgs', multi_class='multinomial', max_iter=1000)\n",
    "\n",
    "lr_classifier.fit(combined_x_train, y_train)\n",
    "\n",
    "# Predict\n",
    "pred_probs = lr_classifier.predict_proba(combined_x_test)\n",
    "\n",
    "# Results\n",
    "display_results(y_test, pred_probs)\n",
    "\n",
    "with open('pred_probas/combined_lr_classifier.pkl', 'wb') as f:\n",
    "    pickle.dump(pred_probs, f)\n",
    "    \n",
    "with open('trained_models/combined/LR.pkl', 'wb') as f:\n",
    "    pickle.dump(lr_classifier, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00126757 0.00218956 0.00360749 ... 0.         0.         0.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = xgb.plot_importance(xgb_classifier, max_num_features=10, height=0.5, show_values=False)\n",
    "fig = ax.figure\n",
    "fig.set_size_inches(8, 8)\n",
    "contribution_scores = xgb_classifier.feature_importances_\n",
    "print(contribution_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy =  0.707\n",
      "Test Set F-score =  0.716\n",
      "Test Set Precision =  0.735\n",
      "Test Set Recall =  0.713\n",
      "Confusion matrix, without normalization\n",
      "[[117  11  81   1   3  11]\n",
      " [ 11 175  54   4   8  48]\n",
      " [ 30  28 470   2   7  46]\n",
      " [  0   0   0 247   0   0]\n",
      " [  0   0   3   0 238   0]\n",
      " [ 12  42 159   6   7 139]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load predicted probabilities\n",
    "with open('pred_probas/combined_rf_classifier.pkl', 'rb') as f:\n",
    "    rf_pred_probs = pickle.load(f)\n",
    "\n",
    "with open('pred_probas/combined_xgb_classifier.pkl', 'rb') as f:\n",
    "    xgb_pred_probs = pickle.load(f)\n",
    "    \n",
    "with open('pred_probas/combined_svc_classifier_model.pkl', 'rb') as f:\n",
    "    svc_preds = pickle.load(f)\n",
    "    \n",
    "with open('pred_probas/combined_mnb_classifier.pkl', 'rb') as f:\n",
    "    mnb_pred_probs = pickle.load(f)\n",
    "    \n",
    "with open('pred_probas/combined_mlp_classifier.pkl', 'rb') as f:\n",
    "    mlp_pred_probs = pickle.load(f)\n",
    "    \n",
    "with open('pred_probas/combined_lr_classifier.pkl', 'rb') as f:\n",
    "    lr_pred_probs = pickle.load(f)\n",
    "\n",
    "with open('pred_probas/combined_lstm_classifier.pkl', 'rb') as f:\n",
    "    lstm_pred_probs = pickle.load(f)\n",
    "\n",
    "# Average of the predicted probabilites\n",
    "ensemble_pred_probs = (xgb_pred_probs +\n",
    "                       mlp_pred_probs +\n",
    "                       rf_pred_probs +\n",
    "                       mnb_pred_probs +\n",
    "                       lr_pred_probs)/5.0\n",
    "# Show metrics\n",
    "display_results(y_test, ensemble_pred_probs)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
